{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synaptic Connections\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/tutorial_toolbox/synaptic_connections.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/tutorial_toolbox/synaptic_connections.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@[Tianqiu Zhang](mailto:tianqiuakita@gmail.com)\n",
    "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn) \n",
    "@[Sichao He](mailto:20301038@bjtu.edu.cn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Synaptic connections is an essential part for building a neural dynamic system. BrainPy provides several commonly used connection methods in the [brainpy.connect](../apis/auto/building/connect.rst) module (which can be accessed by the shortcut `bp.conn`) that can help users to easily construct many types of synaptic connection, inclulding built-in and self-customized connectors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## An Overview of BrainPy Connectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we provide an overview of BrainPy connectors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connector.require()\n",
    "This method returns the connection properties required by users. The connection properties are elaborated in the following sections in detail. Here is a brief summary of the connection properties users can require.\n",
    "\n",
    "| Connection properties | Structure | Definition | \n",
    "| :- | :- | :- |\n",
    "| `conn_mat` | 2-D array (matrix) | Dense connection matrix | \n",
    "| `pre_ids` | 1-D array (vector) | Indices of the pre-synaptic neuron group |\n",
    "| `post_ids` | 1-D array (vector) | Indices of the post-synaptic neuron group |\n",
    "| `pre2post` | tuple (vector, vector) | The post-synaptic neuron indices and the corresponding pre-synaptic neuron pointers | \n",
    "| `post2pre` | tuple (vector, vector) | The pre-synaptic neuron indices and the corresponding post-synaptic neuron pointers |\n",
    "| `pre2syn` | tuple (vector, vector) | The synapse indices sorted by pre-synaptic neurons and corresponding pre-synaptic neuron pointers | \n",
    "| `post2syn` | tuple (vector, vector) | The synapse indices sorted by post-synaptic neurons and corresponding post-synaptic neuron pointers |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users can implement this method by following sentence:\n",
    "```python\n",
    "pre_ids, post_ids, pre2post, conn_mat = conn.require('pre_ids', 'post_ids', 'pre2post', 'conn_mat')\n",
    "```\n",
    "\n",
    "```{note}\n",
    "Note that this method can return multiple connection properties.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connection Properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are multiple connection properties that can be required by users."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. `conn_mat`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The matrix-based synaptic connection is one of the most intuitive ways to build synaptic computations. The connection matrix between two neuron groups can be easily obtained through the function of `connector.requires('conn_mat')`. Each connection matrix is an array with the shape of $(n_{pre}, n_{post})$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-conn_mat.png\" width=\"400 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. `pre_ids` and `post_ids`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using *vectors* to store the connection between neuron groups is a much more efficient way to reduce memory when the connection matrix is sparse. For the connction matrix `conn_mat` defined above, we can align the connected pre-synaptic neurons and the post-synaptic neurons by two one-dimensional arrays: *pre_ids* and *post_ids*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre_ids-post_ids.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this way, we only need two vectors (`pre_ids` and `post_ids`) to store the synaptic connection. `syn_id` in the figure indicates the indices of each neuron pair, i.e. each synapse."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. `pre2post` and `post2pre`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another two synaptic structures are `pre2post` and `post2pre`. They establish the mapping between the pre- and post-synaptic neurons.\n",
    "\n",
    "`pre2post` is a tuple containing two vectors, one of which is the post-synaptic neuron indices and the other is the corresponding pre-synaptic neuron pointers. For example, the following figure shows the indices of the pre-synaptic neurons and the post-synaptic neurons to which the pre-synaptic neurons project:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre2post.png\" width=\"300 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To record the connection, firstly the post_ids are concatenated as a single vector call the **post-synaptic index vector (indices)**. Because the post-synaptic neuron indices have been sorted by the pre-synaptic neuron indices, it is sufficient to record only the starting position of each pre-synaptic neuron index. Therefore, the pre-synaptic neuron indices and the end of the last pre-synaptic neuron index together make up the **pre-synaptic index pointer vector (indptr)**, which is illustrated in the figure below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre2syn-2.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The post-synaptic neuron indices to which pre-synaptic neuron $i$ projects can be obtained by array slicing:\n",
    "\n",
    "```python\n",
    "indices[indptr[i], indptr[i+1]]\n",
    "```\n",
    "\n",
    "Similarly, `post2pre` is a 2-element tuple containing the pre-synaptic neuron indices and the corresponding post-synaptic neuron pointers. Taking the connection in the illutration aobve as an example, the post-synaptic neuron indices and the pre-synaptic neuron indices to which the post-synaptic neurons project is shown as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-post2pre.png\" width=\"300 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **pre-synaptic index vector (indices)** and the **post-synaptic index pointer vector (indptr)** are listed below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-post2syn-2.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When the connection is sparse, `pre2post` (or `post2pre`) is a very efficient way to store the connection, since the lengths of the two vectors in the tuple are $n_{synapse}$ and $n_{pre}$ ($n_{post}$), respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. `pre2syn` and `post2syn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last two properties are `pre2syn` and `post2syn` that record pre- and post-synaptic projection, respectively. \n",
    "\n",
    "For **`pre2syn`**, similar to `pre2post` and `post2pre`, there is a **synapse index vector** and a **pre-synaptic index pointer vector** that refers to the starting position of each pre-synaptic neuron index at the synapse index vector.\n",
    "\n",
    "Below is the same example identifying the connection by pre-synaptic neuron indices and the synapses belonging to them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre2syn.png\" width=\"300 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For better understanding, The synapse indices, pre- and post-synaptic neuron indices are shown as below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre2syn-1.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **pre-synaptic index pointer vector** is computed in the same way as in `pre2post`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-pre2syn-2.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, **`post2syn`** is a also tuple containing the synapse neuron indices and the corresponding post-synaptic neuron pointers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-post2syn.png\" width=\"300 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only different from `pre2syn` is that the synapse indices is (most of the time) originally sorted by pre-synaptic neurons, but when computing `post2syn`, synapses should be sorted by post-synaptic neuron indices:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-post2syn-1.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **synapse index vector** (the first row) and the **post-synaptic index pointer vector** (the last row) are listed below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../_static/syn-example-post2syn-2.png\" width=\"700 px\">"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:19.927763Z",
     "start_time": "2025-10-06T05:18:19.921564Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bp.math.set_platform('cpu')\n",
    "\n",
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:19.969653Z",
     "start_time": "2025-10-06T05:18:19.966386Z"
    }
   },
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Built-in regular connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.One2One\n",
    "\n",
    "The neurons in the pre-synaptic neuron group only connect to the neurons\n",
    "in the same position of the post-synaptic group. Thus, this connection\n",
    "requires the indices of two neuron groups same. Otherwise, an error will\n",
    "occurs.\n",
    "\n",
    "<img src=\"../_static/one2one.png\" width=\"200 px\">"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:19.982169Z",
     "start_time": "2025-10-06T05:18:19.977295Z"
    }
   },
   "source": [
    "conn = bp.connect.One2One()\n",
    "conn(pre_size=10, post_size=10)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One2One"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `pre_size` denotes the size of the pre-synaptic neuron group, `post_size` denotes the size of the post-synaptic neuron group.\n",
    "Note that parameter `size` can be *int*, *tuple of int* or *list of int* where each element represent each dimension of neuron group.\n",
    "\n",
    "In One2One connection, particularly, `pre_size` and `post_size` must be the same."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `One2One` is inherited from `TwoEndConnector`. Users can use method `require` or `requires` to get specific connection properties.\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.010096Z",
     "start_time": "2025-10-06T05:18:20.004616Z"
    }
   },
   "source": [
    "size = 5\n",
    "conn = bp.connect.One2One()(pre_size=size, post_size=size)\n",
    "res = conn.require('pre_ids', 'post_ids', 'pre2post', 'conn_mat')\n",
    "\n",
    "print('pre_ids:', res[0])\n",
    "print('post_ids:', res[1])\n",
    "print('pre2post:', res[2])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre_ids: [0 1 2 3 4]\n",
      "post_ids: [0 1 2 3 4]\n",
      "pre2post: (Array([0, 1, 2, 3, 4], dtype=int32), Array([0, 1, 2, 3, 4, 5], dtype=int32))\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.All2All\n",
    "\n",
    "All neurons of the post-synaptic population form connections with all\n",
    "neurons of the pre-synaptic population (dense connectivity). Users can\n",
    "choose whether connect the neurons at the same position\n",
    "(`include_self=True or False`).\n",
    "\n",
    "<img src=\"../_static/all2all.png\" width=\"200 px\">"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.035952Z",
     "start_time": "2025-10-06T05:18:20.030613Z"
    }
   },
   "source": [
    "conn = bp.connect.All2All(include_self=False)\n",
    "conn(pre_size=size, post_size=size)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "All2All(include_self=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `All2All` is inherited from `TwoEndConnector`. Users can use method `require` or `requires` to get specific connection properties.\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.073454Z",
     "start_time": "2025-10-06T05:18:20.067923Z"
    }
   },
   "source": [
    "conn = bp.connect.All2All(include_self=False)(pre_size=size, post_size=size)\n",
    "res = conn.require('pre_ids', 'post_ids', 'pre2post', 'conn_mat')\n",
    "\n",
    "print('pre_ids:', res[0])\n",
    "print('post_ids:', res[1])\n",
    "print('pre2post:', res[2])\n",
    "print('conn_mat:', res[3])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre_ids: [0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4]\n",
      "post_ids: [1 2 3 4 0 2 3 4 0 1 3 4 0 1 2 4 0 1 2 3]\n",
      "pre2post: (Array([1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2, 4, 0, 1, 2, 3], dtype=int32), Array([ 0,  4,  8, 12, 16, 20], dtype=int32))\n",
      "conn_mat: [[False  True  True  True  True]\n",
      " [ True False  True  True  True]\n",
      " [ True  True False  True  True]\n",
      " [ True  True  True False  True]\n",
      " [ True  True  True  True False]]\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.GridFour\n",
    "\n",
    "`GridFour` is the four nearest neighbors connection. Each neuron connect to its\n",
    "nearest four neurons.\n",
    "\n",
    "<img src=\"../_static/grid_four.png\" width=\"250 px\">"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.120769Z",
     "start_time": "2025-10-06T05:18:20.115617Z"
    }
   },
   "source": [
    "conn = bp.connect.GridFour(include_self=False)\n",
    "conn(pre_size=size)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridFour(include_self=False, periodic_boundary=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `GridFour` is inherited from `OneEndConnector`, therefore there is only one parameter `pre_size` representing the size of neuron group, which should be **two-dimensional geometry**.\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.155610Z",
     "start_time": "2025-10-06T05:18:20.143780Z"
    }
   },
   "source": [
    "size = (4, 4)\n",
    "conn = bp.connect.GridFour(include_self=False)(pre_size=size)\n",
    "res = conn.require('pre_ids', 'conn_mat')\n",
    "\n",
    "print('pre_ids', res[0])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre_ids [ 0  0  1  1  1  2  2  2  3  3  4  4  4  5  5  5  5  6  6  6  6  7  7  7\n",
      "  8  8  8  9  9  9  9 10 10 10 10 11 11 11 12 12 13 13 13 14 14 14 15 15]\n"
     ]
    }
   ],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.570296Z",
     "start_time": "2025-10-06T05:18:20.171487Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(res[1])\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.GridEight\n",
    "\n",
    "`GridEight` is eight nearest neighbors connection. Each neuron connect to its\n",
    "nearest eight neurons.\n",
    "\n",
    "<img src=\"../_static/grid_eight.png\" width=\"250 px\">"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:20.580961Z",
     "start_time": "2025-10-06T05:18:20.575988Z"
    }
   },
   "source": [
    "conn = bp.connect.GridEight(include_self=False)\n",
    "conn(pre_size=size)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridEight(N=1, include_self=False, periodic_boundary=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class `GridEight` is inherited from `GridN`, which will be introduced as followed.\n",
    "\n",
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:21.417712Z",
     "start_time": "2025-10-06T05:18:20.617433Z"
    }
   },
   "source": [
    "size = (4, 4)\n",
    "conn = bp.connect.GridEight(include_self=False)(pre_size=size)\n",
    "res = conn.require('pre_ids', 'conn_mat')\n",
    "\n",
    "print('pre_ids', res[0])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre_ids [ 0  0  0  1  1  1  1  1  2  2  2  2  2  3  3  3  4  4  4  4  4  5  5  5\n",
      "  5  5  5  5  5  6  6  6  6  6  6  6  6  7  7  7  7  7  8  8  8  8  8  9\n",
      "  9  9  9  9  9  9  9 10 10 10 10 10 10 10 10 11 11 11 11 11 12 12 12 13\n",
      " 13 13 13 13 14 14 14 14 14 15 15 15]\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take the central point (id = 4) as an example, its neighbors are all the other point except itself.\n",
    "Therefore, its row in `conn_mat` has `True` for all values except itself."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:21.666099Z",
     "start_time": "2025-10-06T05:18:21.460944Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(res[1])\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.GridN\n",
    "\n",
    "`GridN` is also a nearest neighbors connection. Each neuron connect to its\n",
    "nearest $(2N+1) \\cdot (2N+1)$ neurons (if including itself).\n",
    "\n",
    "<img src=\"../_static/grid_N.png\" width=\"250 px\" >\n",
    "\n",
    "Here are some examples to fully understand `GridN`. It is slightly different from `GridEight`: `GridEight` is equivalent to `GridN` when N = 1.\n",
    "\n",
    "- When N = 1:\n",
    "    $\\begin{bmatrix}\n",
    "    x & x & x\\\\\n",
    "    x & I & x\\\\\n",
    "    x & x & x\n",
    "    \\end{bmatrix}$\n",
    "\n",
    "- When N = 2:\n",
    "    $ \\begin{bmatrix}\n",
    "    x & x & x & x & x\\\\\n",
    "    x & x & x & x & x\\\\\n",
    "    x & x & I & x & x\\\\\n",
    "    x & x & x & x & x\\\\\n",
    "    x & x & x & x & x\n",
    "    \\end{bmatrix} $"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:21.682426Z",
     "start_time": "2025-10-06T05:18:21.677980Z"
    }
   },
   "source": [
    "conn = bp.connect.GridN(N=2, include_self=False)\n",
    "conn(pre_size=size)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridN(N=2, include_self=False, periodic_boundary=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:21.887410Z",
     "start_time": "2025-10-06T05:18:21.697488Z"
    }
   },
   "source": [
    "size = (4, 4)\n",
    "conn = bp.connect.GridN(N=1, include_self=False)(pre_size=size)\n",
    "res = conn.require('conn_mat')"
   ],
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:21.957282Z",
     "start_time": "2025-10-06T05:18:21.892671Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(res)\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Built-in random connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.FixedProb\n",
    "\n",
    "For each post-synaptic neuron, there is a fixed probability that it forms a connection\n",
    "with a neuron of the pre-synaptic population. It is basically a all_to_all projection,\n",
    "except some synapses are not created, making the projection sparser.\n",
    "\n",
    "<img src=\"../_static/fixed_proab.png\" width=\"200 px\" >\n",
    "\n",
    "Class `brainpy.connect.FixedProb` is inherited from `TwoEndConnector`, and it receives three settings:\n",
    "- `prob`: Fixed probability for connection with a pre-synaptic neuron for each post-synaptic neuron.\n",
    "- `pre_ratio`: The ratio of pre-synaptic neurons to connect.\n",
    "- `include_self`: Whether connect to inself.\n",
    "- `allow_multi_conn`: Whether allow one pre-synaptic neuron connects to multiple post-synaptic neurons.\n",
    "- `seed`: Seed the random generator.\n",
    "\n",
    "And there are two parameters passed in for calling instance of class: `pre_size` and `post_size`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:22.376717Z",
     "start_time": "2025-10-06T05:18:21.967005Z"
    }
   },
   "source": [
    "conn = bp.connect.FixedProb(prob=0.5, include_self=False, seed=134)\n",
    "conn(pre_size=4, post_size=4)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False,  True, False,  True],\n",
       "       [ True, False,  True,  True],\n",
       "       [ True,  True, False,  True],\n",
       "       [False,  True,  True, False]], dtype=bool)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 25
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.FixedPreNum\n",
    "\n",
    "Each neuron in the post-synaptic population receives connections from a\n",
    "fixed number of neurons of the pre-synaptic population chosen randomly.\n",
    "It may happen that two post-synaptic neurons are connected to the same\n",
    "pre-synaptic neuron and that some pre-synaptic neurons are connected to\n",
    "nothing.\n",
    "\n",
    "<img src=\"../_static/fixed_pre_num.png\" width=\"200 px\">\n",
    "\n",
    "Class `brainpy.connect.FixedPreNum` is inherited from `TwoEndConnector`, and it receives three settings:\n",
    "- `num`: The conn probability (if \"num\" is float) or the fixed number of connectivity (if \"num\" is int).\n",
    "- `include_self`: Whether connect to inself.\n",
    "- `allow_multi_conn`: Whether allow one pre-synaptic neuron connects to multiple post-synaptic neurons.\n",
    "- `seed`: Seed the random generator.\n",
    "\n",
    "And there are two parameters passed in for calling instance of class: `pre_size` and `post_size`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:27.675026Z",
     "start_time": "2025-10-06T05:18:22.398731Z"
    }
   },
   "source": [
    "conn = bp.connect.FixedPreNum(num=2, include_self=True, seed=1234)\n",
    "conn(pre_size=4, post_size=4)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True,  True, False, False],\n",
       "       [False,  True, False,  True],\n",
       "       [ True, False,  True, False],\n",
       "       [False, False,  True,  True]], dtype=bool)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.FixedPostNum\n",
    "\n",
    "Each neuron in the pre-synaptic population sends a connection to a fixed number of neurons\n",
    "of the post-synaptic population chosen randomly. It may happen that two pre-synaptic neurons\n",
    "are connected to the same post-synaptic neuron and that some post-synaptic neurons receive\n",
    "no connection at all.\n",
    "\n",
    "<img src=\"../_static/fixed_post_num.png\" width=\"200 px\">\n",
    "\n",
    "Class `brainpy.connect.FixedPostNum` is inherited from `TwoEndConnector`, and it receives three settings:\n",
    "- `num`: The conn probability (if \"num\" is float) or the fixed number of connectivity (if \"num\" is int).\n",
    "- `allow_multi_conn`: Whether allow one pre-synaptic neuron connects to multiple post-synaptic neurons.\n",
    "- `include_self`: Whether connect to inself.\n",
    "- `seed`: Seed the random generator.\n",
    "\n",
    "And there are two parameters passed in for calling instance of class: `pre_size` and `post_size`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:28.543114Z",
     "start_time": "2025-10-06T05:18:27.743381Z"
    }
   },
   "source": [
    "conn = bp.connect.FixedPostNum(num=2, include_self=True, seed=1234)\n",
    "conn(pre_size=4, post_size=4)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True, False,  True, False],\n",
       "       [ True,  True, False, False],\n",
       "       [False, False,  True,  True],\n",
       "       [False,  True, False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.FixedTotalNum\n",
    "\n",
    "Connections between pre-synaptic and post-synaptic neurons are determined by \n",
    "a specified total number or propotion of connections.\n",
    "\n",
    "Class `brainpy.connect.FixedTotalNum` is inherited from `TwoEndConnector`, and it receives two settings:\n",
    "- `num`: The total number of connections (if \"num\" is float) or the fixed number of connectivity (if \"num\" is int).\n",
    "- `allow_multi_conn`: Whether allow one pre-synaptic neuron connects to multiple post-synaptic neurons.\n",
    "- `seed`: Seed the random generator.\n",
    "\n",
    "And there are two parameters passed in for calling instance of class: `pre_size` and `post_size`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:28.815825Z",
     "start_time": "2025-10-06T05:18:28.561653Z"
    }
   },
   "source": [
    "conn = bp.connect.FixedTotalNum(num=8, allow_multi_conn=False, seed=1234)\n",
    "conn(pre_size=3, post_size=4)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False,  True,  True,  True],\n",
       "       [False,  True,  True,  True],\n",
       "       [ True, False,  True, False]], dtype=bool)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.GaussianProb\n",
    "\n",
    "\n",
    "Builds a Gaussian connection pattern between the two populations, where\n",
    "the connection probability decay according to the gaussian function.\n",
    "\n",
    "Specifically,\n",
    "\n",
    "$$\n",
    "p=\\exp\\left(-\\frac{(x-x_c)^2+(y-y_c)^2}{2\\sigma^2}\\right)\n",
    "$$\n",
    "\n",
    "where $(x, y)$ is the position of the pre-synaptic neuron\n",
    "and $(x_c,y_c)$ is the position of the post-synaptic neuron.\n",
    "\n",
    "For example, in a $30 \\textrm{x} 30$ two-dimensional networks, when\n",
    "$\\beta = \\frac{1}{2\\sigma^2} = 0.1$, the connection pattern is shown\n",
    "as the follows:\n",
    "\n",
    "<img src=\"../_static/gaussian_prob.png\" width=\"500 px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`GaussianProb` is inherited from `OneEndConnector`, and it receives four settings:\n",
    "\n",
    "- `sigma`: (float) Width of the Gaussian function.\n",
    "- `encoding_values`: (optional, list, tuple, int, float) The value ranges to encode for neurons at each axis.\n",
    "- `periodic_boundary` : (bool) Whether the neuron encode the value space with the periodic boundary.\n",
    "- `normalize`: (bool) Whether normalize the connection probability.\n",
    "- `include_self` : (bool) Whether create the conn at the same position.\n",
    "- `seed`: (bool) The random seed."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:28.926677Z",
     "start_time": "2025-10-06T05:18:28.821329Z"
    }
   },
   "source": [
    "conn = bp.connect.GaussianProb(sigma=2, periodic_boundary=True, normalize=True, include_self=True, seed=21)\n",
    "conn(pre_size=10)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True,  True, False,  True, False, False, False, False,  True,\n",
       "         True],\n",
       "       [ True,  True,  True,  True, False, False, False, False, False,\n",
       "         True],\n",
       "       [ True,  True,  True,  True, False, False, False, False, False,\n",
       "         True],\n",
       "       [False,  True,  True,  True,  True, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False,  True,  True,  True, False, False, False,\n",
       "        False],\n",
       "       [False, False,  True, False,  True,  True,  True,  True, False,\n",
       "        False],\n",
       "       [False, False, False,  True, False,  True,  True,  True, False,\n",
       "        False],\n",
       "       [ True, False, False, False, False,  True,  True,  True, False,\n",
       "         True],\n",
       "       [False,  True, False, False, False, False,  True,  True,  True,\n",
       "         True],\n",
       "       [ True, False,  True, False, False,  True, False,  True,  True,\n",
       "         True]], dtype=bool)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:29.456211Z",
     "start_time": "2025-10-06T05:18:28.930673Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(conn.require('conn_mat'))\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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cSgAAj4/7HxOknzjD5IMVlAzDVCqKwuKd55D61g+UlQE1sw7Fum/fnt0w061S5OslEkmhRWdJnyvqNZmZmUhJSSn2/dIuhst60ZvXcwoKCsU6YjM5Q1TgZuQAoNKgBaq0G47Eh2fx7Z1/zuOabQahWvsRhcZgUy8YeWI/ZQzDVCoGWmrgAUUe9k4J/gc8RRWoNGpd5Bi8/8UpDj6fDyUlJSgpKRXrvrLg38WwrIvegq5JSUkp9v0iUfHPxc6PQCAockEqqVIHZDmq0DaFVbShpGcGVeM2EKhoIvV9ABIfnIFArSo0W/Qu8F4C8DE2pVhfbBimpFhByTBMpaKmJISOhhDfkgovJMSpP5D+8TnUGrfPd4uWvOhrqVaqXqHyXgyLRCKpFb1FvT8WGvhWSG4pwT6Iu7YDuhP/glDz58lMqsZtACIk/HMIaqa2hU7DKO4UD4YpqcrzjscwTKVGRLhx4wa2bt2KEJE+NJrbF7oPZcrru4BEXKzhbgGfBzujWqXMlpEXPp8PRUVFKCoqyiUeEeHTp0+46vcKL14UfG3S0ytQ1G6QU0xmU23YCimvbiEzKgwqBk0LbKO4UzwYpqRYQckwTIWWkpKCo0ePws3NDW/evEGzZs2waPIwuH8ofFPzlKB/wFetCuVCPrT/TSwhDLfOfZ4yU/nExcXh1atXv/wJDAxEUlISeArK0JtztsBFSOLUBPDz2BaIJP+bsyopeO5qSaZeMExJsYKSYZgKKTw8HO7u7tizZw8SExPRt29f7NmzB23btgWPx8PL/f75nuWdrfbIP4sVU8DnoU0DLTSspVHa9JlyJCMjA69fv/6lcHz58iUiIiIAAAoKCjAxMYGFhQUcHBxgYWEBCwsLDD/9vsA9URWq6SLt4zNkxX2FQvU6OY+nBPsAPD4UahoUmFdlm3rBcIv9pDEMU2EQER48eICtW7fiwoULUFdXx/jx4+Hk5AQDA4Nfrl3fzwKdt/gUWFAWNzb/f+0yFZNEIsHHjx9z9Tq+ffs2Z6V7vXr1YGFhgVGjRuUUjkZGRnkOqXc0TsJR/0/5/gxqtnZEWtgTRB5bCI0W9j8X5bx7hPSwJ1C37Aqhhla+ubKpF4y88YhIOu+mDMMwHMnMzMTp06fh5uaGJ0+ewMjICDNnzsTIkSOhrp57yDDbqYBwOHu8kloeP27shPvcYRg6dKjU2mS48f379zyHq1NSUgAA1apVyykYs/+Ym5tDU7Poe5WGRiWhy9a7BV6TERGChHsnkBUVBnFaEoRVtaFu3gma1o6FzgG+Nbs96y1n5Ib1UDIMU25FR0dj9+7d2LVrFyIjI9GtWzdcuXIF3bp1K9KegIOt9BH9Iw2bb78DiIBSbKo9u6Mhnn2riWHDhuHVq1dYt25dsfYlZLiRlpaG4ODgXMVjZGQkAEBRURGmpqawsLCAo6NjTvGoq6tb6k3YG2lroF3DGgVOvVDSNYb2wFXFapdNvWC4wApKhmHKnefPn8PNzQ0nTpyAQCDAqFGjMGPGDDRu3LjYbaUEeCDumg+0e06HBLxiDYHzQVAQCrDawQyDrPRBnQ/C3NwcCxYsQHBwMI4dOwYNDfahXhaIxWKEhYXlKhzfvXsHieTn1jr169eHhYUFxo0bl1M4NmrUCAoKCjLLS9pTLwBAyOexqReM3LEhb4ZhygWxWIy///4bW7duhY+PD/T09ODk5ITx48ejevXqJWrz5cuXaNmyJebOnYup85dh8YVX8H33HQJ+wYVlzvPfXuPWhvFoqFP1l+cvX76MIUOGoF69evDy8kL9+vVLlB9TMlFRUbkKx6CgIKSlpQEAtLS0cg1Xm5mZcVb8S3vqhUt/CwyyYjsNMPLFCkqGYcq0Hz9+YP/+/dixYwc+fPiA3377DTNnzkS/fv0gFJZ8kCUzMxOtW7eGSCTC48ePczblDo1KwnH/cHi/jUZ4bOovJ+rw8HPlrJ1RLbSpJUbPts2xceNGzJkzJ1f7wcHBcHBwQEJCAs6fPw9bW9sS58rkLSUlBUFBQbmKx5iYGACAsrJyznD1v//o6OiUuTPDd3iHwvXGWxBRqXKb39UY0+waSjEzhikaVlAyDFMmhYaGYtu2bTh48CAyMzMxaNAgzJw5Ey1btpRK+8uXL8cff/yBR48eoVmzZnlek5IhwsfYFGSKJFAU8mGgpfbLNixTpkzB6dOn8f79e1SrVi3X/bGxsfj999/h6+sLd3d3TJw4USq5VzYikQjv3r3LVTiGhYXlFGCGhoa5CseGDRtCICh8v9GyID09HU36TUGmRR8IFBSLNQQu4PMg5PNypl4wDBdYQckwRZSVlYWoqCikpqbix48fqFGjBqpVq4aqVatynVqFQUS4desW3NzccPnyZdSsWROTJ0/GlClTULt2banFCQgIgI2NDZYvX47ly5eXuJ3IyEg0bNgQU6ZMwaZNm/K8JisrC7NmzcLOnTsxffp0bN68uVQ9qxUZESEyMjJX4RgcHIz09HQAQK1atXIVjqamplBTK98beM+bNw/bt2/HZR8/HA7KLNLUCz4PkBDQQDUTh6d1g151VTlmzDC/YgUlw+QjPT0d169fx4MHD+Dn54eAgICcOVj/1rBhQ1hbW8PGxgZdunRBo0aNOMi2fEtNTcWxY8ewbds2BAUFwdLSEjNnzsSQIUOgrFz0M7SLIj09Hc2bN4eqqioePnxY6gUXq1evxrp16xASEpJrr8t/27VrF6ZPnw47OzucOXMmzx7NyiQ5ORmBgYG5isfY2FgAgKqqKszMzHIVj7VqVby9FX19fWFra4sNGzZgwYIFAIo+9cL/uCtCn/jizZs37IsKwylWUDLMf6SkpGDPnj3YtGkTvn37Bl1dXdjY2MDGxgaNGzeGiooKfvz4AXV1dURFRcHf3x9+fn549uwZxGIxBgwYgMWLF6Np06Zcv5Qy78uXLzmn2cTHx6NPnz6YOXMmbG1tZTbHbf78+di+fTuePHkCMzOzUreXkpKCRo0awc7ODsePHy/wWm9vbwwYMABaWlrw8vKCiYlJqeOXdSKRCG/fvs1VOH748AHAz7O0GzVqlKtwbNCgQaXYdik5ORmWlpaoXbs2fHx88hyiz556ERj8BiOGDcGtCyfRvk1rAMCzZ8/QvHlzHD58GCNHjpR3+gzz/4hhmBwHDhygGjVqkFAopNGjR1NwcHCR701JSaE9e/ZQ/fr1CQD16dOHoqOjZZht+SSRSOjBgwc0aNAgEggEpKmpSbNnz6b379/LPLavry/xeDzauHGjVNvdu3cvAaCAgIBCr3337h01btyYqlSpQlevXpVqHlySSCT0+fNnunLlCrm4uNDw4cPJ0tKSFBUVCQABoNq1a1PXrl1p7ty5dOjQIXry5AmlpqZynTqnJk+eTKqqqhQaGlrotTExMQSAzp49+8vjDg4O1KhRI8rKypJVmgxTKFZQMgwRZWRk0NSpUwkAjRw5kj58+FDitrKysujIkSNUs2ZN0tfXp6dPn0ov0XIsIyODjh8/Tq1atSIA1LBhQ9q2bRslJibKJX5ycjIZGhpSmzZtSCQSSbXtrKwsMjU1pQ4dOpBEIin0+h8/fpC9vT3x+XzasmVLke4pSxISEujevXu0a9cumjp1KrVr146qVq2aUziqqalR69atacKECbRt2zby9vammJgYrtMuc65du0YAyN3dvUjXSyQSUlZWps2bN//y+OPHjwkAHT16VBZpMkyRsIKSqfTi4+OpXbt2pKCgQH/99ZfU2g0PD6cWLVqQiooKnTt3TmrtljfR0dG0du1aql27NgGgzp07099//01isViueUybNo1UVFTo7du3Mmn/0qVLBIAuXbpUpOtFIhHNnz+fANDYsWMpPT1dJnmVRkZGBr18+ZKOHz9Ozs7OZG9vT/r6+jmFo0AgoMaNG9PAgQNpzZo1dPHiRXr//r3c/23Lo7i4ONLV1aUuXboU6wtFo0aNaPbs2bke79WrFxkbG0v9yxLDFBUrKJlKTSQSUY8ePahq1ap07949qbefmppKAwcOJEVFRfLz85N6+2XZixcvaOzYsaSkpETKyso0YcIEevXqFSe53Lp1iwDQ9u3bZRZDIpGQnZ0dmZqaFmvo8fDhw6SoqEht27alqKgomeVXEIlEQh8/fqS///6b1q9fT0OGDCFzc3NSUFDIKR7r1KlD3bt3p/nz59ORI0fo2bNnZbIILi+GDx9OVapUofDw8GLd16lTJxowYECuxwMCAggAHT9+XFopMkyxsEU5TKW2aNEibNy4EVevXkXXrl1lEiMjIwN2dnb49OkTHj9+LNXtb8oasViMy5cvY+vWrfD29kadOnXg5OSECRMmQEtLi5Ocfvz4gSZNmqBhw4a4efOmTBd6PHnyBC1btsSePXswYcKEIt/38OFD9OvXD0pKSvDy8oKlpaXMcoyPj8+1QCYwMBCJiYkAAE1NTZibm+daJFPZV6VLk4eHBxwdHUu0kGbMmDF4/fo1/Pz8cj1nb2+PsLAwBAYGlpv9N5kKhOuKlmG4kj1EuWnTJpnHioiIIF1dXWrfvn25my9XFD9+/KCtW7dSgwYNCABZW1vTqVOnKDMzk+vUaOzYsaShoUEfP36US7yhQ4eSjo4OJScnF+u+8PBwatasGampqZGHh0ep80hPT6fnz5/T0aNHacGCBdSjRw+qW7duTo+jUCgkc3NzGjJkCK1fv57+/vtv+vjxY4X8+SxLIiMjqUaNGtS3b98S/V0vW7aMdHV183zO39+fANDJkydLmybDFBsrKJlKSSQSkampKXXq1EluH6A3btwgAHTx4kW5xJOH0NBQmjFjBmloaJBQKKQhQ4aUqaH97C8N+/btk1vMDx8+kKKiIq1atarY9yYnJ9OAAQMIAK1Zs6ZIP5tisZjCwsLI09OT1q5dS4MGDSJTU1MSCAQ5xaO+vj7Z29uTs7MzHT9+nF6+fEkZGRkleXlMKUgkEurbty/VqFGjxNMb9uzZQzweL98va927dydTU1M2j5WRO1ZQMpXSoUOHirzNizR17NiRzMzMyvXEeYlEQrdu3aLevXsTj8cjLS0tWrJkCX358oXr1H7x/ft30tHRoZ49e8q9123evHmkpqZG3759K/a9YrGYVq5cSQBo8ODBlJKSkvPc9+/fydvbm7Zt20YTJkwga2trUldXzykcq1atSu3ataOpU6fSrl276N69e5SQkCDNl8aUwuHDhwkAnT9/vsRtXL16lQDk2+P+8OFDAkCnT58ucQyGKQlWUDKVjkgkIgMDA3J0dJR7bD8/PwJAJ06ckHvs0kpNTaW9e/eSubk5ASALCwvat29fmd1HcMiQIVStWjX6+vWr3GPHxcVRtWrVaNKkSSW6Py0tjVxcXEhBQYG0tbXJ1tY2Z5U8AFJUVCRLS0saPnw4ubi40JUrV+jz589suLoMCw8PJ01NTRo+fHip2gkMDCQAdPfu3Xyv6dq1K5mZmbFeSkauWEHJVDo+Pj4EgB4+fMhJ/Hbt2lHPnj05iV0SX758ocWLF5OWlhbxeDxycHCg27dvl+ni5ezZs5yveP3zzz9JIBAUuDm+WCym0NBQ8vDwoFWrVtGAAQPI2NiY+Hz+L1vzKCkp0ejRo+nUqVMUFBRUJuamMkUnkUioc+fOVKdOHYqLiytVWz9+/Cj0Z/v+/ft5boDOMLLEVnkzlc7MmTNx/vx5hIeHc3K0m5ubG+bPn4+YmBhUqVJF7vGLyt/fH25ubjh79ixUVFQwduxYODk5oWHDhlynVqDo6GiYmZmhffv2OHfunMyOcCxMRkYGGjduDAsLC3h6eiI6OjrX6uqgoCCkpqYCALS0tHKtrDYzM0NKSgr69++Pp0+fYv/+/Rg2bBgnr4cpuZ07d2LatGm4du0aunXrVur2qlatikWLFmHhwoX5XtOlSxdERUXh+fPnleIIS6YM4LqiZRh5kkgkVLduXZo+fTpnOYSHh3Pee5afzMxMOnnyJFlbWxMAMjQ0pK1bt9KPHz+4Tq1Ishc91KxZk7M9HVNSUujRo0e0f/9+6tGjR87cRvyvx1FJSYmaNWtGI0eOpE2bNtG1a9coIiKiwB7f9PR0GjVqFAEgZ2dnNpRZjoSGhpKqqipNnjxZam2am5vTtGnTCrzG19e31PM1GaY4WA8lU6m8f/8eDRs2xJUrV9CjRw/O8mjSpAmsra2xZ88eznL4t9jYWOzZswfu7u74+vUrOnbsiJkzZ8Le3r5c7Wd37NgxjBgxAufPn0f//v1lGkssFuP9+/e5eh3fvXsHIgKPx0P9+vURFxcHVVVVbN68GZaWlmjYsCGEQmGx4xERNm/ejPnz56N37944duwYNDQ0ZPDKGGkRi8WwtbXFt2/f8OLFC6irq0ul3Z49e0JBQQGenp4FXtepUyfExsbi6dOnrJeSkbniv6sxTDkWEhICADAzM+M0D1NT05xcuBQUFAQ3NzccPXoURIThw4djxowZaNKkCdepFdvXr1/h5OSEYcOGSbWYJCJERUXlKhyDg4ORlpYGAKhZsyYsLCzQs2fPX4ar1dTU4OPjgw4dOoCIYGJiUuI8eDwe5s6di8aNG2Pw4MFo06YNvLy8UL9+fWm9VEbK/vzzTzx48AA+Pj5SKyYBQE9PDwEBAYVet2LFCtja2sLLywt9+/aVWnyGyQvroWQqlc2bN2Pp0qVITk7m9Bv7ypUrsXv3bkRGRso9tkQiwZUrV+Dm5oZbt25BV1cXU6dOxcSJE1GzZk255yMNRISePXvi5cuXCAwMLPGpLsnJyQgKCspVPH7//h0AoKKiAjMzs1xzHbW1tQts18HBAYGBgXj9+jWUlJRKlNu/BQcHw8HBAQkJCTh//jxsbW1L3SYjXYGBgWjRogVmzJiBTZs2SbXttWvXws3NDTExMYVea2dnhx8/fuDJkyeczSdmKgfWQ8lUKqGhoWjUqBHnwz/GxsaIiopCYmIiNDU15RIzKSkJhw4dwrZt2/Du3Tu0atUKJ06cgKOjIxQVFeWSg6zs27cP165dw5UrV4pUTIpEIoSGhuYqHMPCwgAAfD4fDRs2hIWFBZycnHIKxwYNGpRoCoCLiwssLCywc+dOzJ49u9j3/5epqSn8/f3x+++/o3PnznB3d8fEiRNL3S4jHZmZmRg5ciQaNmyINWvWSL19PT09fP/+HampqVBVVS3w2hUrVsDOzg5///03HBwcpJ4Lw2RjBSVTqaSnp0t16KmksnNIT0+XeUEZFhaGHTt2YP/+/UhJScGAAQNw9OhRWFtbyzSuvHz8+BFz5szB+PHjc82LJSJERETkKhxfv36NjIwMAICOjg4sLCzQt2/fnMLR1NQUKioqUsuxcePGGD9+PNasWYPRo0dL5VxsLS0tXL9+HbNmzcKkSZMQGBiIzZs3l2h+JiNda9euxatXr+Dn5wdlZWWpt6+npwcA+PLlC4yMjAq8tkOHDmjfvj1WrVqF3r17s15KRmbYOw/DVEBEBB8fH2zduhVeXl6oVq0apk6diqlTp+Z8GFUEEokEY8aMgZaWFlasWIEHDx7kKh7j4+MBAGpqajA3N0fLli0xZsyYnOKxRo0acsl15cqVOHbsGNavXy+1IVAFBQW4u7vD3Nwc06dPx+vXr3HmzBmpFKxMyQQEBGD9+vVYtmwZWrRoIZMY+vr6AIDPnz8XWlACP3spO3XqhMuXL6NXr14yyYlh2BxKplIZM2YM3r59i/v373OaR/bwU1RUFGrVqiW1dtPT03Hy5Els3boVL1++hKmpKWbOnInhw4cXOjRWXmRlZSEkJASvXr3CgQMHcOvWLWhrayMqKgoAIBAIYGRklGueo4GBAedTHVatWoX169cjJCQEBgYGUm3b29sbAwYMgJaWFry8vEq1AIgpmbS0NDRv3hxqamp4+PAhFBQUZBInPT0dKioqOHjwIEaPHl3o9USE9u3bIz09HY8ePWK9lIxMsB5KplJRVlZGcnIy12kgKSkJAKQ2HPbt2zfs3LkTf/31F2JiYmBvb48///wTnTp1KrcfHkSEL1++4OXLl7/0OL558wZZWVk51+nr62PQoEE5haOJiYlMhhmlYe7cudi9ezeWLl2KY8eOSbVtOzs7PHr0CL1794a1tTVOnTqF7t27SzUGU7AlS5bgw4cPePr0qcyKSeDn+0bNmjXx+fPnIl3P4/GwYsUKdOnSBVevXkXPnj1llhtTebGCkqlUGjVqhMOHD0MikXDaW/X27Vtoa2uXev7k48ePsXXrVpw5cwaKiooYO3Yspk+fjkaNGkkpU/lISEhAYGBgruHqHz9+AAA0NDRgbm4OGxsbTJw4Eaampli4cCF+/PiB58+fl5veV3V1daxevRoTJ07E7NmzpT4kamhoCD8/PwwdOhT29vZwdXXFrFmzyu2XivIke4rJpk2bYGpqKvN4enp6RS4ogZ97UrZp0warVq1Cjx492M8EI3VsyJupVK5cuQJ7e3t8+vQpZx4SFwYPHoxv377Bx8en2PeKRCJ4eHjAzc0NDx48gIGBAWbMmIGxY8eW6aMcgZ+rX9+8eZOrcMz+YBQKhTA2Ns41XF2vXr1fPgBdXFywePFi+Pr6ok2bNly9nBIRiUSwtLSEtrY2bt++LZMPdrFYjEWLFmHTpk0YO3Ysdu7cKZXtipi8JSUloUmTJtDT04O3t7dcDgPo27cvMjIycPXq1SLfc+PGDXTr1g1Xr15lvdeM1LEeSqZSMTY2BvBzQ28uC8rg4OBir7KOi4vD3r174e7ujs+fP8PW1hYXLlxA7969y9xpNkSET58+5SocQ0JCIBKJAPzsYbGwsMDQoUN/Ga4ubAujwMBALF++HPPmzSt3xSTws2h2cXFB7969ZTb8KBAIsHHjRpibm2PChAl4+/Ytzp8/L9X5usz/mzt3LmJiYnD79m25/S7q6enhzp07xbqnS5cusLa2xqpVq9CtWzfWS8lIFeuhZCoVIkK9evXQp08fbN++vdj3p2SI8DE2BZkiCRSFfBhoqUFNqXjfy8LDw1GvXj0cP34cQ4cOLfT64OBgbNu2DUeOHIFYLMawYcMwY8YMNG3atNj5y0JcXFyuwjEwMDBnnmiVKlVy9Tiam5ujatWqxY6VlZWF1q1bIyMjA0+ePCmzcyULQ0To2LEjYmJi8Pz5c5lu9fPw4UP069cPSkpK8PLygqWlpcxiVUbZXwp2796NSZMmyS3uxo0bsXbtWiQmJhbrvmvXrqFHjx64fv06unbtKqPsmMqIFZRMpTNr1iycO3cO4eHhRZpHGRqVhOP+4fAOiUZ4XCr+/QvDA6BfXRV2xrUwrLU+GmkXfraym5sbFixYgJiYmHznUEokEly/fh1bt27FjRs3oKOjg6lTp2LSpEmc9TKlp6fj9evXuYrHiIgIAD+3sGncuHGu4rFu3bpS6wlZuXIl1q5dC39/f5ltySIvT548QcuWLbF3716MHz9eprE+f/6MPn364O3btzh69Cj69esn03iVRVxcHMzNzdGkSRNcvXpVrj1+p06dwpAhQ5CQkFCsqS5EBGtrawiFQty7d4/1UjJSwwpKptK5e/cubG1t8eDBA9jY2OR73ee4VCy+8Aq+775DwOdBLMn/VyX7+XYNa2B9PwvoVc9/kUj79u2hoaGBy5cv53ouOTkZhw8fxrZt2/D27Vu0bNkSM2fOxMCBA+V2mo1EIsGHDx9yFY6hoaEQi8UAAAMDg1yFo5GRkUxXtj558gTW1tZYsmQJVq5cKbM48jRs2DB4e3sjNDQUampqMo2VkpKC0aNH49y5c1izZg2WLFnCiolSGjZsGK5cuYLAwEDUqVNHrrHv37+Ptm3b4tWrVzA3Ny/WvdlzyW/evInOnTvLKEOmsmEFJVPpiMViNGzYEM2bN8f58+fzvOZUQDhWeAVBJKECC8n/EvB5EPJ5WOVghsFWuedo+vn5wcbGBidOnMCQIUNyHv/48SN27NiBffv2ITk5Gf3798fMmTPRpk0bmX7ox8TE5Cocg4KCkJKSAgCoXr16rsLRzMxMbsdFZsvIyECLFi2gqKgIf39/mRau8vTx40cYGxtj6dKlWLZsmczjSSQSrFmzBitXrsTgwYOxf//+crNCvqw5d+4cfv/9dxw7dgzDhg2Te/zsqTNXrlzJdUJUYYgIrVq1grKyMu7evcu+WDBSwQpKplI6fPgwRo8ejUePHsHKyuqX53Z4h8L1xttSx5jX1QhOdv+/fQ8RoVOnToiOjsaLFy/A5/Ph6+sLNzc3XLx4EVWqVMGECRMwbdo0qS8YSktLQ3BwcK49HbM3A1dSUoKpqWmu4rF27dpl4sPG2dkZW7ZswePHj2FhYcF1OlI1b948/PXXX3j37h20tbXlEvPcuXMYOXIkTE1N4enpKffetfIuMjIS5ubmsLW1xblz5zj5HRGJRFBSUsKuXbtKdI77pUuX0Lt3b9y+fRsdO3aUQYZMZcMKSqZSEovFaNKkCXR0dHDr1q2cD4RTAeFw9ngltTgu/S0w6H89ldlbdpw7dw7Jyclwc3PDs2fPYGJigpkzZ2LEiBGlHvYUi8UICwvL1ev47t07SCQS8Hg8NGjQIFfh2LBhwzJ7BvSDBw/Qrl07rFu3Ds7OzlynI3Xx8fEwNDTEoEGDsGvXLrnFffbsGfr06YOsrCxcvHgRrVu3llvs8oyI0LdvXzx8+BBBQUGoWbMmZ7nUrVsXY8aMwZo1a4p9LxHBysoKampqJdq+jGH+ixWUTKWVPY9o48aNmD9/Pj7HpaLzFh9kiCT53pMR+Q4/7p1AxpdgkCgLwqraUG/aHZotHfK8XknIx63ZthBm/ECzZs2grKyM1NRUxMTEoEePHpg5cya6dOlSok3Wo6Ki8hyuTktLAwDUqFEjz+FqdXX1YsfiSmpqKpo2bQotLS34+vqW2aK3tDZv3owFCxYgMDBQrkcmRkZGon///nj69Cn279/PydBteXPo0CGMGTMGFy5cQN++fTnNxcbGBsbGxjh06FCJ7vfy8kKfPn3g7e2NDh06SDU3pvJhBSVTqS1evBguLi64cuUKjn2pigdhsfnOmUz78BTR51ZDUdsQaibtwFNUhighEiAJqtmNzfMeAZ8HUy0h/P4Yhu/fv0NFRQVjxozBjBkzcvbELExKSgqCgoJyFY8xMTEAfh7DZmZmlqt41NbWLhPD1aUxc+ZM7N27F8+fP4eRkRHX6chMRkYGTExM0KRJE3h6eso99qRJk3D48GE4Oztj3bp1nJ95XlaFh4fDwsICffv2xeHDh7lOBwMHDsT379+LvR9lNiJCixYtUKVKFXh7e0s5O6ayYQUlU6mJxWI4ODjA7/VHaAzamO91koxUfN0zEUp1GqNmv0Xg8Yr3gft172RMGdoPq1atQrVq1fK8RiQS4d27d7kKx7CwMBAReDweGjZsmKtwNDQ0LHMbm0uDt7c3OnbsiK1bt2LmzJlcpyNzJ0+exNChQ+Hj44P27dvLNTYRYfPmzZg/fz569eqF48ePQ0Oj8C2wKhOJRIIuXbrg7du3ePXqVYn2UZW2efPmwdPTE6GhoSVu4+LFi+jXrx8nP3dMxcIKSqbSS0hIQLtZ25Co3RQ8ft6FWdKzK4i7vhO643dBoYYeJJnp4CkoFqmwJIkYbbUlOD6n78//JsK3b99yFY7BwcHIyMgAAGhra+cqHE1NTSvNitzExEQ0adIEBgYGuHPnTqXoMZNIJGjdujX4fD78/Pw46V2+cuUKhgwZAn19fXh5eaF+/fpyz6Gs2rFjB6ZPn44bN26gS5cuXKcD4Oeets7OzkhNTS3xzwsRoVmzZtDS0sLt27elnCFTmbCCkmEAtN90B+Fxafk+H3NhPdI+PkfNfosRd2MXRHFfwVNQhpq5Hap3mgCesOA9IqsJRWibcCuneIyLiwMAqKqqwtzcPFfxyOVE/7Jg4sSJOHnyJF6+fFmpipp//vkHdnZ2OHXqFAYNGsRJDsHBwXBwcEBCQgLOnz8PW1tbTvIoS96+fYumTZtizJgxcHd35zqdHB4eHnB0dER0dHSp3jOy2/H19UXbtm2lmCFTmbCCkqn0kjNEsFh5HQX9IkTsd4Io4RsAQL1JVyjrWyA9/BWSnvwN1cbtUbPPggJjEBFULi1BE1PjXwrH+vXrV4ret+LIPsrur7/+KtF2KOVd7969ERQUhNevX0NJSYmTHGJjYzFw4EDcvXsX7u7ulfLfIZtYLEbbtm0RExODFy9eyHwD+uIICAhAq1at8OTJEzRv3rzE7UgkEjRt2hTa2tq4efOmFDNkKpOKuWSSYYrhU2xKgcUkAFBWOigrA+rNeqB6l5/n9aoatwGJs5D8/Bqy2g2DQvX89/Lj8Xg4f+MuzHSLfkRaZRQfH4/x48ejW7dumDBhAtfpcMLFxQUWFhbYtWsXZs2axUkOWlpauHbtGmbNmoVJkyYhMDAQmzdvrrCr7AuyadMm+Pv7w9fXt0wVkwCgp6cH4OfRmqUpKPl8PpYvX47ff/8dDx48QJs2baSVIlOJsK4RptLLLGCboGzZQ9pqjX8d/lMz7QAAyPj6RipxKrsZM2YgJSUF+/btK/cr1EvK1NQU48ePx5o1a5CQkMBZHgoKCnB3d8fOnTuxc+dO9OjRA/Hx8Zzlw4VXr15h+fLlmD9/Pn777Teu08mlVq1aUFBQwOfPn0vdVv/+/WFubo5Vq1ZJITOmMmIFJVPpKQoL/zUQqGv9/F+1qr8+rvazx1GSniyVOJXZhQsXcOzYMWzfvh1169blOh1OrVq1ChkZGVi/fj3XqWDKlCm4efMmnj59itatW+PNm8K/PFUEmZmZGDFiBIyMjLB69Wqu08kTn89H3bp1ER4eLpW2li1bhhs3bsDPz08K2TGVDfuEYyo9Ay01FNYXpqhjCAAQJcX+8rgo6efiGoFqwUPZvP/FYfIWExODSZMmoW/fvhg+fDjX6XBOR0cH8+bNw7Zt2/Dp0yeu04GdnR0ePXoEoVAIa2trXLt2jeuUZG716tUICgrC0aNHOZvLWhT6+vpS6aEEgAEDBsDU1JT1UjIlwgpKptJTUxJCv3rB2/GombQDACS/vPHL48kvbwB8AZT0Cz5fWl9LFWpKlW/+WVEQESZPngyJRILdu3dX2qHu/5o3bx6qVauGpUuXcp0KAMDQ0BB+fn5o27Yt7O3tsWXLFlTUNZ3+/v74448/sHz5cjRr1ozrdAqkp6cntYIyu5fy2rVrePTokVTaZCoPVlAyDAA741oQ8PMvZBR1DKHWpAtSg30Qc9EFSU8vI+biBqQG+0CzdX8INbTyvVfA58HOqJYs0q4QTp48CQ8PD+zevRva2tpcp1NmqKurY9WqVTh27BiePn3KdToAAE1NTXh6emLu3LmYM2cOxo0bl7N3akWRlpaGUaNGoUWLFli0aBHX6RRKmgUlAPz+++8wMTFhvZRMsbFtgxgGQGhUErpsvVvgNSQW4cfDM0h+eQvi5DgIq9SERvNe0LTqU2j7t2a3R8Na7OSR/4qIiIC5uTm6d++OEydOcJ1OmSMSidCkSRPUrl0bt27dKlO9t0eOHMGECRNgZWUFDw8P1KpVMb40zZo1C7t378azZ8/QuHFjrtMp1K5duzB9+nRkZGRI7cSsEydOYNiwYXj06BGsrKyk0iZT8bGCkmH+Z8R+/wLP8i4JAZ+HNg20cHRca6m1WVEQEXr16oWnT58iKCgI1atX5zqlMunvv/+Gg4MDLl++jJ49e3Kdzi8ePnyIfv36QUlJCV5eXrC0tOQ6pVLJPu5z8+bNmD17NtfpFMmlS5fQu3dvfPnyBXXq5L91WXGIxWKYmZmhUaNG+Pvvv6XSJlPxsSFvhvmf9f0sICxg2LskhHwe1vcreH5lZXXw4EFcuXIFe/fuZcVkAXr16gVbW1ssWLAAYrGY63R+YWNjg4CAAGhpaeG3337DhQsXuE6pxBITEzFmzBi0b9++XJ0d/++9KKVFIBBg6dKluHTpEp48eSK1dpmKjRWUDPM/etVVscrBTKptrnYwg14hC34qo0+fPmHWrFkYM2YMevXqxXU6ZRqPx4OrqyuCgoJw6NAhrtPJRU9PD76+vujZsyf69++PtWvXlsvFOnPmzEFsbCwOHTpUrk6vkkVBCQCDBw9Go0aNyuyWSUzZU35+axhGDgZb6WNeVyOptDW/qzEGWelLpa2KRCKRYOzYsahatSq2bNnCdTrlQsuWLTFkyBAsW7YMKSkpXKeTi5qaGk6fPo1Vq1Zh2bJlGDp0KFJTU7lOq8guX76M/fv3Y/PmzeXu7Phq1apBTU1NKntR/ptQKMTSpUvh5eWFZ8+eSbVtpmJiBSXD/IeTXSNs6G8BJSG/wJXfeRHweVAS8uHS3wLT7BrKKMPybdeuXbhz5w4OHDiAKlXYUZRFtW7dOsTGxmLz5s1cp5InHo+H5cuX4+zZs/D09ET79u3x9etXrtMqVGxsLMaPH48ePXpg/PjxXKdTbDweT+orvbMNHToUDRs2ZL2UTJGwgpJh8jDYSh+3ZtuiTYP/nZBTSGGZ/XQjTcKt2basZzIfoaGhWLBgAaZOnYrOnTtznU65Ur9+fTg5OWHjxo2IioriOp18DRgwAPfv30d0dDRatmwJf39/rlMqkJOTEzIyMsr1cZ+yKiiFQiGWLFmCixcv4sWLF1Jvn6lY2CpvhilEaFQSjvuHw/ttND7FpgD/OleHh5+blncwqomr2xZDmPod9+/fL7cfTLIkFovRvn17REVF4fnz51BXV+c6pXInLi4OhoaGGDJkCHbu3Ml1OgWKjIxE//798fTpU+zfvx/Dhg3jOqVczpw5g0GDBuHEiRMYMmQI1+mU2Lhx4/Dq1SuZbEYuEolgbGyMpk2b4vz581Jvn6lAiGGYItuyfSep1G5ETz7FUeDXBEpOz8p57vLlywSA7ty5w2GGZdemTZuIx+ORr68v16mUa66uriQQCOj169dcp1Ko9PR0GjVqFAEgZ2dnEovFXKeUIyIigqpXr04DBgwgiUTCdTqlsmLFCtLR0ZFZ+/v37ycA9OLFC5nFYMo/1kPJMMWwZs0auLu7IzIyMtdzRIQWLVqgevXquHXrFgfZlV3BwcFo3rw5nJyc4OrqynU65VpGRgZMTExgaWmJixcvcp1OoYgImzdvxvz589GrVy8cP34cGhrcbvJPRHBwcEBAQAACAwNRo0YNTvMprf3792PChAlIT0+HoqKi1NvPysqCsbExWrRogbNnz0q9faZiYHMoGaYY4uLioKWV9zGLPB4Pixcvxu3bt8v8vDF5ysrKwqhRo9CgQQOsXbuW63TKPSUlJaxbtw6enp7w9fXlOp1C8Xg8zJ07F5cuXYKPjw9sbGwQFhbGaU4HDx7EpUuXsGfPnnJfTAI/51ASkcwWQSkoKGDx4sU4d+4cAgMDZRKDKf9YQckwxRAbG1vgJtz9+vWDsbEx1q9fL8esyrYNGzbg2bNnOHz4MJSVlblOp0IYPHgwWrRogXnz5pWbPR979uyJhw8fIj09Ha1atYKPjw8neXz8+BGzZs3C6NGj4eDgwEkO0qav/3MRoLS3Dvq3kSNHol69elizZo3MYjDlGysoGaYYYmNj8+2hBH6eMLFo0SJ4eXnh1atXcsysbHr27BlWr16NRYsWsTOBpYjP58PV1RWPHj0qV0OQpqam8Pf3h6WlJTp37ow9e/bINX72HqjVqlXD1q1b5RpblmS1ufm/KSoqYvHixTh79iyCg4NlFocpv1hByTDFEBcXV+gxgUOHDoWBgUGl76XMyMjAqFGjYGZmhmXLlnGdToXToUMH9OrVC4sWLUJGRgbX6RSZlpYWrl27hkmTJmHSpEmYMWMGRCKRXGLv2LED3t7eFW4PVDU1NVSrVk2mBSUAjB49Gnp6eqyXkskTKygZphgK66EEfs43WrBgAc6cOYPQ0FA5ZVb2rFq1Cm/evMGRI0dkslCAAVxcXPDx40fs2rWL61SKRUFBATt27MDOnTuxc+dO9OjRA/Hx8TKNGRISgoULF8LJyQmdOnWSaSwuyGovyn9TVFTEokWLcPr0abx+/VqmsZjyhxWUDFMMBS3K+bcxY8agVq1acHFxkUNWZY+fnx9cXFywcuVKNGnShOt0KixTU1OMGzcOa9asQUJCAtfpFNuUKVNw8+ZNPH36FK1bt8abN29kEkckEmHUqFHQ09OrsL+T8igogZ/vbXXq1GEL7JhcWEHJMEUkkUgQHx9f6JA3ACgrK2Pu3Lk4cuSIXN7ky5LU1FSMGjUKLVu2xIIFC7hOp8JbtWoVMjIy8Mcff3CdSonY2dnh0aNHEAqFsLa2xrVr16QeY+PGjQgICMDhw4ehqqoq9fbLAnkVlEpKSli0aBFOnTqFkJAQmcdjyg9WUDJMESUkJEAikRSphxIAJk+eDA0NDWzatEnGmZUtS5YsQXh4OA4fPgyhUMh1OhVe7dq1MW/ePLi5ueHTp09cp1MihoaG8PPzQ9u2bWFvb48tW7ZIbfX6ixcvsHLlSixYsAA2NjZSabMskldBCfw8mad27dqsl5L5BSsoGaaI4uLiAKDIBaW6ujpmzpyJvXv3lumzl6XJx8cHbm5uWL9+PUxMTLhOp9KYN28eqlatiqVLl3KdSolpamrC09MTc+fOxZw5czBu3LhSLzbKyMjAyJEjYWJigpUrV0on0TJKX18fcXFxSElJkXksJSUlODs748SJE3j79q3M4zHlAysoGaaIYmNjAaBIQ97Zpk+fDqFQWKG2KMlPUlISxowZg7Zt22LmzJlcp1OpqKurY9WqVTh27BiePXvGdTolJhAIsHHjRhw+fBjHjx9Hp06dEB0dXeL2Vq1ahdevX+PIkSNQUlKSYqZljzy2Dvq38ePHQ0dHB+vWrZNLPKbsYwUlwxRRdkFZ1B5KAKhWrRqmTp0Kd3d3ma9i5dr8+fMRHR2NgwcPgs9nby3yNm7cODRu3Bjz588vN5ud52fkyJH4559/8O7dO1hZWeHFixfFbiN7YdiKFSvQtGlT6SdZxsi7oFRWVsbChQtx/PhxvHv3Ti4xmbKNveszTBFlD3kXp4cSAObMmYOsrCzs2LFDFmmVCdevX8dff/0FV1dXGBoacp1OpSQUCuHi4oLbt2/LZGGLvNnY2CAgIABaWlpo06YNLly4UOR7/70wbOHChTLMsuyoU6cOAPkVlAAwYcIE1KxZk/VSMgBYQckwRRYbGwsVFRWoqKgU6z5tbW2MHz8eW7duRXJysoyy405CQgLGjRuHLl26YNKkSVynU6n16tUL7du3x4IFCyAWi7lOp9T09PTg6+sLe3t79O/fH2vXri1S76uzs3OlWximpKQEbW1tuRaUKioqWLhwIY4ePYr379/LLS5TNrGCkmGKqCibmudn/vz5SExMlPtRc/Iwc+ZMJCUlYf/+/eDxeFynU6nxeDy4uroiMDAQhw4d4jodqVBTU8Pp06exatUqLFu2DEOHDkVqamq+19+5cwfbt2/Hhg0bKt3CMHmu9M42adIk1KhRo9KfDMawgpJhiqyom5rnRV9fHyNGjICrqyvS09OlnBl3PD09ceTIEbi5ueXM4WK4ZWVlhcGDB2P58uVyWfErDzweD8uXL8fZs2fh6emJ9u3b4+vXr7mu+/HjB8aMGYMOHTpg+vTpHGTKLS4KShUVFSxYsABHjhzBhw8f5BqbKVtYQckwRRQbG1vs+ZP/5uzsjKioqArTc/T9+3dMnDgRvXv3xqhRo7hOh/mX9evXIyYmBlu2bOE6FakaMGAA7t+/j+joaLRs2RL+/v6/PD979mzEx8dX2oVh+vr6nBykMHnyZFSvXp31UlZyle83jmFKqDRD3gBgZGSE33//HS4uLsjKypJiZtyYNm0aRCIR9uzZw4a6y5j69evDyckJLi4uFW4P1GbNmuHRo0eoX78+bG1tcfz4cQDA33//jYMHD2LLli0wMDDgNkmO6OnpITw8XO6r/FVVVTF//nwcOnQIHz9+lGtspuxgBSXDFFFphryzLV68GB8/fsTJkyellBU3Tp8+jTNnzmDnzp3Q0dHhOh0mD0uXLoVQKMSqVau4TkXqdHR04O3tjcGDB2P48OGYOXMmJkyYAHt7e4wdO5br9Dijp6eHlJQUTs51nzJlCqpVq1ZujwBlSo8VlAxTRKUd8gaAJk2aoHfv3vjjjz8gkUiklJl8RUZGYurUqRg4cCAGDRrEdTpMPqpXr44lS5Zgz549FfLMZSUlJRw8eBCbNm3Ctm3bEBcXhy1btlTq3nJ570X5b2pqapg3bx4OHjyI8PBwucdnuMcKSoYpotIOeWdbvHgx3rx5U6x99coKIsKECRMgFArh7u7OdTpMIZycnFC3bl04OztznYpM8Hg81K1bF8DPfTj79euHsLAwjrPiDpcFJQBMnToVVapUYb2UlRQrKBmmCLKyspCUlCSVgtLa2hodO3bEunXryt2JJocPH8alS5ewd+9e1KhRg+t0mEIoKytj3bp1uHjxInx9fblOR+oiIiIwdepUDBo0CI8fP0Z6ejpatWoFHx8frlPjRO3atSEQCDgrKNXV1TF37lzs37+fsxwY7rCCkmGKoKSn5ORnyZIlePbsWbk60eTz58+YOXMmRo0aBQcHB67TYYpoyJAhaN68eYU4kvHfsnvLlZSU4O7uDlNTU/j7+8PS0hKdO3eukHu+FkYgEEBXV5fTYm7atGnQ0NDAhg0bOMuB4QYrKBmmCEpyjndB7OzsYG1tXW622SAijB07FhoaGti6dSvX6TDFwOfz4erqCn9/f5w7d47rdKRm//79uHLlCvbu3Zvze6mlpYVr165h0qRJmDRpEmbMmAGRSMRxpvLF1dZB2TQ0NDB37lzs27cPX7584SwPRv5YQckwRSDtHkoej4fFixfj3r17uHv3rlTalKXdu3fj1q1bOHDgAKpWrcp1Okwx2dnZwd7eHosWLUJmZibX6ZTahw8fMHv2bIwdOxa9evX65TkFBQXs2LEDu3btwq5du9CjRw/Ex8dzlKn8ZW8dxCUnJyeoqanBxcWF0zwY+WIFJcMUgbR7KIGf5y43adIE69atk1qbsvD+/XvMnz8fkydPRteuXblOhykhFxcXfPjwAbt27eI6lVKRSCQYM2YMtLS0Cty4ffLkybhx4waePn2K1q1b482bN3LMkjtcnJbzX5qampgzZw727t2LiIgITnNh5IcVlAxTBNkFpbR6KIH/76W8ceMGAgICpNauNInFYowZMwa1atXCpk2buE6HKQUzMzOMHTsWq1ev5mSfQmnZtm0bfHx8cPDgQWhqahZ4rZ2dHR49egQFBQVYW1uXqznLJaWnp4cvX75wvi3Z9OnToaKiwnopKxFWUDJMEcTFxUFTUxNCoVCq7Q4YMABGRkZldi6lm5sb7t27h4MHD0JdXZ3rdJhSWr16NdLT06W2YCItLQ3v37/Hy5cvce/ePbx9+zbny5csvHnzBosWLcKMGTNgZ2dXpHsMDQ3x8OFDtG3bFvb29tiyZUuFWpz0X3p6esjMzERMTAyneVSpUgWzZ8/Gnj178O3bN05zYeSEGIYplLOzM9WvX18mbR84cIAAUGBgoEzaL6ng4GBSUlKiWbNmcZ0KI0XLly8nJSUl+vTpU7Hv/fHjBx0/fpycnJyoRYsWJBQKCUCuPw0aNKChQ4fS9u3b6d27d1LJOysri6ysrMjIyIhSUlKKfb9IJKL58+cTABozZgylp6dLJa+y5vHjxwSAAgICuE6F4uPjqUqVKuw9pJLgEVXgr2oMIyUTJ07Es2fPZDI0nZmZiYYNG6J9+/Y4duyY1NsvCZFIhDZt2iAxMRHPnj2DiooK1ykxUpKUlISGDRuiW7duOHLkSJHuiYuLg5ubG7Zt24aEhAQ0atQINjY2sLa2hrGxMZSUlJCamgpFRUV8+/YNfn5+ePjwIZ49ewaxWIyBAwdi8eLFsLCwKHHea9euxYoVK/DgwQO0bt26xO0cOXIEEyZMgJWVFTw8PFCrVq0St1UWRUdHQ1tbGx4eHujXrx/X6WDlypU583fZMa0VHNcVLcOUB46OjtS1a1eZtb99+3bi8/lS680prXXr1hGfzyc/Pz+uU2FkYNeuXcTj8ejp06cFXieRSMjFxYXU1dVJRUWFZs2aReHh4UWOk5KSQjt37qR69eoRAPr9998pLi6u2Pk+ffqUhEIhLV68uNj35uXBgwekra1N+vr69Pz5c6m0WVZIJBJSUlIiNzc3rlMhIqK4uDjS1NSkOXPmcJ0KI2OsoGSYIujQoQMNGTJEZu2npqaStrY2TZgwQWYxiur58+ekoKBAixYt4joVRkaysrLIxMSEOnXqRBKJJM9rkpKSaMCAAQSAZs2aRVFRUSWOl5mZSQcOHKBq1apRw4YNizW9Iz09nczNzcnS0pIyMjJKnMN/hYeHU7NmzUhVVZU8PDyk1m5Z0LBhQ5o7dy7XaeRYtmwZqaioUGRkJNepMDLEFuUwTBFI6xzv/KioqGDOnDk4dOgQvn79KrM4hcnMzMTIkSNhYmKCFStWcJYHI1tCoRAuLi64ffs2rl+/nuv5iIgItGnTBteuXYOHhwe2bNlSqqFhBQUFjBkzBo8fP4aysjKsra1x9erVIt27YsUKhISE4MiRI1BUVCxxDv+lp6cHX19f2Nvbo3///lizZk2FWaxTFrYO+rdZs2ZBKBTC1dWV61QYWeK6omWY8qBOnTq0fPlymcb48eMHVa1aldMJ7EuXLiWhUEjPnj3jLAdGPiQSCbVv357Mzc1JJBLlPJ6WlkZWVlZUp04dmSwUS0pKInt7e1JTU6OXL18WeO39+/eJz+fT+vXrpZ5HNolEQqtWrSIANGjQoBIt+ClrRo4cSTY2Nlyn8YslS5aQqqpqqXq6mbKNFZQMUwTKyspymZO0fPlyUlFRoejoaJnH+i9/f38SCAS0evVqucdmuOHv708AaP/+/UT0s7gaNWoUKSsr0+PHj2UWNzk5mSwtLalBgwYUGxub7zUNGzYka2trysrKklku2c6dO0eqqqrUokUL+vLli8zjydKSJUuobt26XKfxi+/fv5O6ujotWLCA61QYGWFD3gxTiNTUVKSnp8t0yDvbjBkzwOfz5X5edlpaGkaNGoVmzZrB2dlZrrEZ7rRq1QqDBg3CsmXLkJKSgkOHDuHw4cPYt28fWrRoIbO4ampquHjxIn78+IExY8bkec3ChQvx9etXHD58WOr7v+bF0dER9+/fR3R0NFq2bAl/f3+Zx5QVPT09RERElKlzzLW0tDB9+nS4u7vj+/fvXKfDyAArKBmmENI+x7sgWlpamDJlCnbs2CHX00yWLVuGDx8+4PDhw1BQUJBbXIZ769evR0xMDDZu3IglS5Zg6NChGDZsmMzjGhgYYNeuXfDy8sp1nv2tW7fg7u4OFxcXGBkZyTyXbE2bNkVAQADq168PW1tbHD9+XG6xpUlPTw8SiaTMbSg+Z84cAMCff/7JcSaMTHDdRcowZd3z588JAPn7+8slXkREBCkpKdG6devkEu/u3bvE4/Fo06ZNconHlD2zZ88mRUVFEgqF9P79e7nFlUgk1KJFC2rTpk3OavOEhATS09Ojjh07klgsllsu/5aenk6jRo0iAOTs7MxZHiX16tUrAkD379/nOpVcFi5cSOrq6vT9+3euU2GkjPVQMkwhsnso5THkDQC1a9fG2LFjsWXLFqSkpMg0VnJyMkaPHo02bdpg9uzZMo3FlF2zZs1CVlYWjI2N0aBBA7nF5fF4WL9+PR48eJCz2nzmzJlISEjAgQMHwOdz8xGlpKSEgwcPwtXVFS4uLujbty+SkpI4yaUk9PT0AKBMrfTONnfuXEgkEmzevJnrVBgpYwUlwxQi+2xieQx5Z1uwYAHi4+Oxd+9emcZZuHAhIiMjcejQIQgEApnGYsquR48egYjw+vVrhISEyDV2ly5dYG5ujmPHjsHT0xOHDx+Gm5sb6tWrJ9c8/ovH42Hu3Lm4dOkSfHx8YGNjg7CwME5zKqoqVapAQ0MD4eHhXKeSS82aNTFt2jRs374958s6UzGwgpJhChEbGws+n48qVarILaaBgQGGDx+OTZs2ISMjQyYxbt26hZ07d2Ljxo1o2LChTGIw5cP58+fRpEkT6OnpYdGiRXKNzePx4OjoCC8vL0yYMAG9e/fG6NGj5ZpDQXr27ImHDx8iPT0drVq1go+PD9cpFUlZ24vy3+bNmwexWIwtW7ZwnQojRaygZJhCxMXFoXr16nIffnN2dsa3b9+KfN5ycfz48QNjx45Fx44dMWXKFKm3z5QfGRkZuHz5MgYMGIB169bhwoULuHfvnlxz6N+/P5KSkpCeno49e/aAx+PJNX5hTE1N4e/vD0tLS3Tu3Bl79uzhOqVCZReUIpEI79+/x8uXL3Hz5k0EBgbi06dPkEgknOVWq1YtTJkyBdu2bUN8fDxneTDSxQpKhilEbGysXIe7s5mYmMDR0REbNmyQ+vYfs2fP5nyeGlM2PHv2DElJSejRoweGDBmC5s2bY/78+XI9NebVq1cAgE6dOkFHR0ducYtDS0sL165dw6RJkzBp0iRMnz69TG3Lk00kEuHy5cv49u0bbt26hSpVqqBhw4awtLRE165dYWFhAQMDA9SoUQM9evTA6tWr4evrK/dTgubPn4+srCy5b5HGyA77JGGYQsj62MWCLF68GGFhYTh9+rTU2rx06RIOHjyIrVu3cj5PjeFe9pzJxo0bg8/nY9OmTfDz88O5c+fkEv/r169wcnKCjo5OmeuZ/C8FBQXs2LEDu3btwu7du9GjR48y08OWmZmJffv2wdjYGL169cKHDx+QlZWFVatW4ebNm/Dz88Pt27fx4MEDXL58GbNmzQIAbNmyBe3bt0erVq3g6ekpt55LbW1tTJ48GW5ubnLdIo2RIY5XmTNMmefg4ED29vacxe/ZsyeZmppKZeuS79+/k46ODvXs2TNnmxamclu0aBHp6en98ljPnj3J0NCQMjIyZBpbIpFQ9+7dqXbt2jR+/Hhq3LixTONJ0507d6h69erUqFEjev36Nae5XLlyhfT09IjH45GjoyM9fvyYDhw4QAAoPT29wHvFYjFdv36d2rdvTwCoSZMm9Pz5c7nk/e3bN1JWVqaVK1fKJR4jW6yHkmEKwWUPJQAsWbIEwcHB8PT0LHVbTk5OyMjIwN69e8t8bxAjH+/evcu1ebiLiws+fPiA3bt3yzT23r17ce3aNezbtw9NmjTBu3fvOJ3bVxx2dnZ49OgRFBQUYG1tjWvXrsk9ByLCH3/8AXt7e5ibmyMwMBDnzp1DixYtoK+vDwD48uVLgW3w+Xx07doVPj4+uHv3LgQCAWxsbKQ6KpIfHR0dTJo0CVu3bsWPHz9kHo+RLVZQMkwhuC4o27RpA1tbW6xfv75U85zOnj2LU6dOwd3dHbq6ulLMkCnP0tPToaam9stj5ubmGDNmDFavXi2z4ciwsDDMmTMHEyZMQM+ePaGuro6srCy5z+UrDUNDQzx8+BBt27aFvb09tmzZIrf8s7KyMHjwYCxevBjLli3DpUuXYGpqmvN89l6Uxdk6qF27drh37x769euHwYMHY9myZVLP+78WLFiAtLQ0bNu2TeaxGNliBSXDFCJ7lTeXlixZgsePH+PmzZsluj8qKgpTpkyBo6MjBg8eLOXsmIpo9erVSEtLw4YNG6TetkQiwejRo1GzZs1yfwyfpqYmPD09MXfuXMyZMwfjxo2T2VZf/zZv3jx4eHjg3LlzWLVqVa7FdXXr1gVQ/M3NVVVVcezYMaxfvx5r166V+Yp2XV1dTJw4EVu2bEFiYqJMYzGyxQpKhikAESEuLo7THkoA6Ny5M6ysrLBu3bpi30tEmDRpEvh8Pnbt2sWGupki0dXVxdy5c7F161apb5C9detW3Lt3D4cOHYKGhoZU2+aCQCDAxo0bcfjwYRw/fhydOnVCdHS0zOIdPHgQ27Ztg5ubGxwdHfO8RlVVFVpaWiXai5LH42HRokWYMmUKnJyccP/+/dKmXKCFCxciJSUF27dvl2kcRrZYQckwBUhMTIRIJOK8oOTxeFiyZAnu3r1b7D0Cs08g+euvv1CzZk0ZZciUVyoqKvkeKzh//nxUqVJFqkOfwcHBWLx4MWbNmgVbW9ucx5OSkqCoqFiut7EaOXIk/vnnH7x79w5WVlZ48eKF1GMEBwdj8uTJGD9+fKF7yJZ2c/OtW7fC2toaAwYMkOkcxzp16mDChAnYvHlzuTrikvlV+f3NZRg5yD4ajOshbwDo3bs3zM3NsX79+iLf8+XLF0yfPh3Dhw9Hv379ZJgdU141atQIb9++zfM5DQ0NrFy5EkePHsXz589LHSsrKwujRo1C/fr1c/W2h4SEoFGjRuW+B93GxgYBAQHQ0tJCmzZtcOHCBam2v2TJEujq6mLHjh2F/l2VtqBUVFTEyZMnkZCQAFdX1xK3UxTOzs5ITk7Gjh07ZBqHkR1WUDJMAbLP8ea6hxL4uRpz0aJFuHr1Kp4+fVro9USEcePGQU1NjU14Z/JlbGyMr1+/5tszNH78eBgZGUlls/M//vgDz549w+HDh6GiovLLcyEhITAxMSlV+2WFnp4efH19YW9vj/79+2PNmjVSWazj7++PixcvYvXq1VBSUir0en19/VIfv1inTh3MmDEDW7ZsQVRUVKnaKkjdunUxbtw4/Pnnn0hOTpZZHEZ2WEHJMAUoSwUlAAwcOBCGhoZF6qXcu3cvbty4gf3796NatWpyyI4pj7KLuODg4DyfV1BQgIuLC27duoXr16+XOM6TJ0+wZs0aLFq0CK1atfrlOSJCcHAwjI2NS9x+WaOmpobTp09j1apVWL58OYYMGYLU1NRStblixQqYmZlh6NChRbpeWud5L1y4EEKhEBs3bix1WwVxdnZGYmIi3N3dZRqHkRGuNsBkmPLgxIkTBICSkpK4TiXH3r17icfjUXBwcL7XhIWFkZqaGk2YMEGOmTHlUUZGBlWpUoWWL1+e7zUSiYTatWtHFhYWJBKJfnkuOT2LAr8m0NNPcRT4NYGS07Ny3Z+WlkZmZmbUrFmzPDdLf/r0KQGgGzdulP4FlUHnzp0jVVVVatGiBX358qVEbURGRhKPx6O9e/cW+Z7jx48TAEpMTCxRzH+bOXMm1a5dWyoHLBRk0qRJVKNGjTL1nssUDeuhZJgCxMbGQlFRMdc+fVwaOXIk6tSpgz/++CPP5yUSCcaMGYMaNWqU+y1ZGNlTVFSEg4MDzp8/n+81PB4PmzZtwqtXr3DkyBGERiVhpVcQbDd5w3zlddhvv4d+ux7Afvs9mK+8DttN3ljpFYTQqJ/D6MuXL0doaCgOHz4MRUXFXO2fP38e1apVQ4cOHWT1Mjnl6OiI+/fvIzo6Gi1btoS/v3+x2/D09ASPx0OfPn2KfE/2XpTS6KV0dHTEt2/f4OfnV+q2CrJo0SL8+PEDu3btkmkcRvpYQckwBcje1LwsLRRQVFTEvHnzcOLECXz48CHX89u3b4ePjw8OHjxYIbZkYWSvf//+CAoKwps3b/K9pnXr1nAYOhYr/olBl613cdT/Ez7FpeK/MwMJwKe4VBz1/4QuW++i15/XsGXvUaxevRoWFha52iUinD9/Hn369IGCgoJ0X1gZ0rRpUwQEBKB+/fqwtbXF8ePHi3W/h4cHbG1ti7VTgzQLyjZt2kBbW7vALx7SUK9ePYwePRqbNm1CSkqKTGMx0sUKSoYpQFnY1DwvEyZMQPXq1XPNaQoJCYGzszNmzJgBOzs7jrJjyptu3bqhZs2a2LRpU77XnAoIx+v6juDp/JxzKZYUvMgk+/nA6EzUmbALeh0G5XndpUuX8ObNG4wYMaKE2Zcf2tra8Pb2xpAhQzB8+HAsWrSoSEdNSiQS3Lt3D927dy9WvDp16oDH40mloBQIBOjWrRt8fX1L3VZhFi9ejPj4eJkf/clIFysoGaYAXB+7mB9VVVXMnj0bBw4cQEREBABAJBJh9OjR0NPTy3c4nGHyoqKigiVLluDQoUN59lLu8A6Fs8crZIoJPL6geI3zBYBAAYsvBmGHd+gvT0kkEixZsgR2dnaV5guQkpISDhw4AFdXV7i4uKBv376F7r349etXpKSkwMzMrFixFBQUoKOjI5WCEgBMTU0REhIi8+MlDQwMMGrUKGzcuLHUC5kY+WEFJcMUoKwWlAAwdepUqKio5MyTdHV1xaNHj3Do0CGoqqpynB1T3kyePBl169bFkiVLfnn8VEA4XG/kvU9lcbneeIvTAf9/6s7x48fx6tUrrF+/vkxNK5E1Ho+HuXPn4tKlS/Dx8YGNjQ3CwsLyvT4kJAQASrQKXhpbB2UzNjZGYmKiTLcPyrZ48WLExsbir7/+knksRjpYQckwBSirQ94AUKVKFUyfPh27d++Gr68vli9fjvnz56NNmzZcp8aUQ0pKSnBxcYGHh0fOh/jnuFSs8ArK956MyHeIOr0c4Zt/R/jm3xF1ahkyo/IvjABguVcQPselIiQkBE5OThg4cCCsra2l+lrKi549e+Lhw4dIT09Hq1at4OPjk+d1oaGhEAqFMDAwKHYMaW0dBABGRkYAkO9G+NLUoEEDjBw5Ehs3bkRaWprM4zGlxwpKhilAWe6hBICZM2eCiDBw4EAYGRlh1apVXKfElGODBw/GtGnTMH36dNy/fx+LL7yCKJ+5khmR7xB1bAFECZGo+tsQVPltMLLiIxB5whlZsV/yjSGSEOaffYY+ffpAV1cXe/fuldXLKRdMTU3h7+8PS0tLdO7cGXv27Ml1TUZGBpSUlCAUCovdvjQLyuzdLjIyMqTSXmGWLFmCmJiYPP9OmLKHFZQMU4DY2Ngy20MJADVq1ECTJk0QGRkJd3f3Ip2ewTAF2bJlC2xsbOA41gm+777nu/jmh+8x8ISK0BnpCs3W/VGltSN0RmwCiJDgcyTf9sUSgt/HBESn8+Hp6QlNTU1ZvZRyQ0tLC9euXcOkSZMwadIkTJ8+HSKRSCpt6+npITw8XObzHmXB0NAQw4cPh4uLC9LT07lOhykEKygZJh9isRgJCQlluofyyZMnePz4Mfh8Ph4+fMh1OkwFoKCggHPnzqF6KweQRJzvdemfg6Bs0BQClf8vCIXq1aGsZ47U948gycx/mJIkYvRfuCVnCJX5+fe+Y8cO7Nq1C7t370b37t0RFxdX6nb19PSQlpYmlba4sGTJEkRFRVX6nuzygBWUDJOP+Ph4AGXn2MX/Sk9Px8iRI9GkSROMHTsWmzdvZisiGamoWbMmaljYFriim8RZ4Alzb1LOU1ACxCJkxXzK914eX4DQlNz3Mj8XR924cQPPnj1D69at8ebNG6ioqCA9PR1ZWVnFbk+ae1Fmr0b/7znsstSoUSMMGzYMGzZsYL2UZRwrKBkmH9nneJfVIe/ly5fj3bt3OHLkCBYtWoS4uDjs37+f67SYCiA5Q4TP8QUvhFCoXhcZESG/9GKSOAsZET9XJIuSYgu8Pzw2FSkZ0hnWrWjs7Ozw6NEjKCoqonXr1khISIBYLC5wJXh+pFlQlma1eWksXboUkZGR7P2tjGMFJcPkI3uIqCz2UD548ACurq5YvXo1zM3N0aBBAwwZMgQbN25EZmYm1+kx5dyn2JRcJ+D8l0bznhDFfUXslW3I/B6OzJiP+H5pM8TJP3v2SVTwzyEB+BjLTkLJj6GhIR4+fIj27dtj0aJFAIDXr18Xux0dHR0oKChIpaB88+YNqlWrhho1apS6reIwMjLCkCFDsGHDBrktCGKKjxWUDJOP7B7KslZQpqSkYNSoUWjdujXmzZuX8/iiRYvw5csXHD16lMPsmIogU1T46S0azXpC02YgUoJ98G3fVHzb7wRRfCQ0rR0BAHxFZanEqcw0NTVx8eLFnN/zNWvWFLug4vP5qFOnjlQKyuDgYBgbG3OyZ+jSpUvx9etXHDhwQO6xmaJhBSXD5CO7h7KsDXk7Ozvj69evOHz4MASC/5/jZmpqiv79+2PDhg1SWyHKVE6KwqJ9NFSzHYm6M45Be5gLao/dgdqjtwD0s0gUVq9T6P1HDh7AiRMn4Ofnh+jo6HK5ElnWBAIBNm7ciGbNmuHZs2fo1KkToqOji9VGnXoN8DoyCc/C4xEU8aNEUw2ysrJw7do1zk40MjExweDBg/HHH3+wXsoyqvibWjFMJREbGws1NbUytRXPnTt3sGPHDri5ueW5Qnbx4sVo2bIlzp49iyFDhnCQIVMRGGipgQcUOuwNAAJldQj0/v9IwPSPzyHQqAEFrboF30iEY7u3YGvk15yH1NTU0KBBg1/+GBoaokGDBqhXrx6UlQvv9ayoZs6cidGjRyMkJARWVlbw8vKCpaVlvteHRiXhuH84vEOi8cV6Nr7weOi36wEAgAdAv7oq7IxrYVhrfTTS1ig0/t27dxEXFwdHR0dpvaRiW7ZsGczMzHDo0CFMmjSJszyYvPGIfSVkmDwtXboUR48exadP+a9WlafExERYWFigQYMGuH37Nvj8vHuRunfvjq9fv+LFixf5XsMw+fn27RsuXbqETa/VkKlUpVj3pry+i++eG1HNbiw0W/cv8Np6WqrwmWeHxMREfPjwAWFhYTl/3r9/j7CwMHz8+DFnZTOPx0OdOnVyFZzZf2rVqlWhj2+Mi4uDtrY2VqxYAQ8PD4SEhODYsWPo16/fL9d9jkvF4guv4PvuOwR8Xr77iALIeb5dwxpY388CetXzP7J1ypQpuHr1Kj58+MDp3/PgwYPx8OFDhIaGQlGR7RRQlrCCkmHyMXXqVDx8+BDPnj3jOhUAwIQJE3Dq1Cm8evWqwCPYfH190b59e3h6esLBwUF+CTLlEhEhODgYXl5e8PT0hL+/P/h8PkxHrEBy7eYg5F08pIcH4sf9k1Cu3wx8FU1kRrxB8stbUK7fDLUGLC9wyyFIxGhVPQMHnXrmnL6SF7FYjK9fv/5SbP77T0xMTM61qqqq+RabBgYGct3qRlb69euHV69e4fHjx5g4cSLOnj2L1atXY+nSpeDxeDgVEI4VXkEQSajAQvK/BHwehHweVjmYYbCVfq7nIyMjYWhoiDlz5mDNmjXSfEnFFhQUBAsLC/z111+YMGECp7kwv2IFJcPkY9CgQYiNjcWtW7e4TgVXrlyBvb099uzZU6Q30fbt2yMjIwN+fn4VuteGKRmRSIT79+/nFJHv37+HmpoaunXrhj59+sDe3h5xIkV02Xo33zay4r8h7sZOZEa+hyQzDcKq2lA37wTNVn3BEygUmkPEvilQyfyB4cOHY+LEiQUO3+YnKSkpV+9m9p8PHz78suOBrq5uvgWnjo5Oufg9efXqFSwtLbFz505MmjQJa9aswYoVKzBo0CC0GrMc2/75UOoY87oawcmu0S+POTk54fjx4wgLC0O1atVKHaO0Bg4ciICAALx9+xYKCoX/rDHywQpKhslH586doaWlhdOnT3OaR1xcHMzNzWFpaYkrV64U6YPv+vXr6N69O27evInOnTvLIUumrEtOTsb169fh5eWFy5cvIzY2FrVr10bv3r3Rp08fdOzYMdccxRH7/fEgLLZYvV2FEfB5aNNAC6s71sL+/fuxf/9+REZGolWrVpg4cSIGDRoEdXX1UseRSCSIiIjIt3czKioq51oVFRXUr18/z2Kzfv36UFXNfyhY3oYPH447d+7g7du3UFdXx/nz5zHR5RA0Ok2WWgyX/hYY9L+eyvfv36Nx48ZYvXo1nJ2dpRajNF69eoUmTZpg3759GDduHNfpMP/DCkqGyUfz5s3RunVr7Nq1i9M8hg8fjsuXLyMwMBB16hS+chb4OYxpZWUFTU1N3LlzR8YZMmXVt2/f4OXlBS8vL9y+fRsZGRkwNzeHg4MD+vTpg5YtWxY4z/ZzXCo6b/FBhhS391ES8nFrtm3OfL2srCxcvnwZe/bswbVr16Curp7Ta9m0aVOpxf2vlJSUAns3/30qi46OTr69m7Vr15brXOWwsDBYWlqie/fuOHPmDL7Ep6HTZm9kiCjPL5skykKC7zGkBHlDkp4MhZoGqNp+BFTqN8s3Rva/kZYy0LZtW8THxyMwMLDA6QnyNmDAADx9+hQhISGsl7KMYAUlw+SjXr16GDFiBNauXctZDufPn8eAAQNw9OhRDB8+vFj3enh4wNHREQ8ePICNjY2MMmTKEiJCUFAQPD094eXlhUePHkEgEKBdu3ZwcHCAg4MDDA0Ni9XmqYBwOHu8klqO/+79+q9Pnz7l9FpGRETAysoKEydOxODBg6XSa1lUEokEkZGR+fZufvv2LedaJSWlfHs3GzRoIJMi7MKFC+jfvz/WrVuH19qdCuxFjvHciNSQ+9Bs2QfC6rpIeXULGd9CoT1kPZT/tTr/3wR8HmwaaEFy2w2enp548OBBiaYkyNLLly9haWmJAwcOYMyYMVynw4AVlAyTL3V1daxZswazZ8/mJH50dDTMzMzQtm1beHh4FHuOl0QigYWFBerXr49Lly7JKEuGayKRCPfu3cspIsPCwqCuro7u3bvDwcEBPXv2LPXm/Du8Q+F6422pc53f1RjT7BoWep1IJMKVK1fw119/4erVq1BTU8OwYcMwceJENG/evNR5lFZqaio+fvyYb8GZlvb/x1Zqa2vnW2zq6uqWuHdzxYoV+GPnIeiO35nvNRkRIYg8MhdV7caiyv9W3ZMoExH7pkGgVgU6I1wLjPF172Qc3/knBg4cWKIcZa1///54+fIl3rx5A6GQ7YLINVZQMkweMjIyoKysjEOHDmHUqFFyj09EGDBgAO7evYvAwEBoa2uXqJ1jx45hxIgRePbsmUyHDxn5SkpKwvXr1+Hp6YnLly8jPj4eurq6Ob2QdnZ2Ut+zsbQriFc7mOXbM1mQ8PDwnF7Lr1+/okWLFpg0aRIGDx4MDY3C90+UNyJCVFRUvsXm16//v++moqJigXM3C3p9EokEdvN24qOCfr4r6uO9DyDx0UXozToFvtL/zwP98fAMEnyOoM7UgxBq1sz7dUjEaKaWhIvLh5Xwb0L2nj9/jmbNmnH2Ps38ihWUDJOHb9++QVdXF3///Td69eol9/jHjx/H8OHDcfbsWQwYMKDE7YhEIhgZGaFly5Y4c+aMFDNk5C0iIiJnVfadO3eQmZkJCwsL9OnTBw4ODmjRooXM5/LJao/DohCJRLh69Sr27NmDK1euQFVVFUOHDsXEiRPRokWLUrUtT+np6QX2bqak/P/55jVr1sy3d7NOnTrouPkuPsWl5hsr6tRSiJNioTvh13ngaR+fI/rUUtR0XAbVRq3zvT97r9CyrG/fvggKCsLr169ZLyXHWEHJMHkIDAyEhYUFJ/MPIyIiYGZmhh49euDEiROlbu+vv/7ClClT8Pr1axgbG0shQ0YeiAiBgYHw9PSEp6cnHj9+DIFAgPbt26NPnz7o3bs3GjRowEluOaewvI1GeGxqrhN1suIi4GBliLkOVmhYS/q9iJ8/f87ptfzy5QuaN2+OiRMnYujQoWWy17KoiAgxMTH5FptfvnzJOZ5SUVUDtaefAAqYChOxbyoEalWhPWT9L49nfg/Ht31TUb3bNGg065Hv/TwAgSu7QU2p7BZqT58+RYsWLXDkyBGMGDGC63QqNVZQMkwe7t69C1tbW7x580auRRgRwd7eHs+ePUNQUJBUzhHPyMhAgwYN0LVrVxw8eFAKWTKykpWV9ct8yA8fPkBdXR09evRAnz590KNHjzJ3tnxKhggfY1OQKZJAUchHHU1F6OtqY+HChViyZIlMY4tEIly7dg179uzB5cuXoaKi8kuvZXnYW7I4MjIy8OnTJ4SFheHhm884HKVb4PVfd4+HsHodaA9c9cvjWQmRiNg9HtU6TYCmVZ8C27g8vS3MdIt3YpK8OTg4ICQkBMHBwRAICthQn5Epdi4bw+QhNjYWAEq9mKG49u/fj6tXr2Lfvn1SKxyUlJQwb948HDt2rMwcI8n8v8TERJw9exbDhw+HtrY2OnbsiHPnzqF79+64du0avn//jjNnzmDYsGFlrpgEADUlIcx0q6CZfjWY6VZBVXUVdOzYEdevX5d5bKFQiF69esHLywufPn3CggULcO3aNVhZWaFFixbYvXs3EhMTZZ6HvCgpKcHIyAjdu3dH3/6FT4XhCRUBcVaux0mU+f/PFyJTiltGycqKFSvw9u1bnDp1iutUKjVWUDJMHrILyqpVq8ot5sePHzF79myMHTsW9vb2Um174sSJqFKlCjZu3CjVdpmS+fLlC3bt2oXu3bujZs2aGDhwIF69egUnJyc8fvwYnz9/xs6dO9GtWzcoKSlxnW6xdevWDQ8fPpRrMVe3bl0sX74cHz58wOXLl6Gnp4dp06ahdu3aGD9+PB49eoSKNCCnKCz841ugXh3i5Phcj4uT43Kel0YcrrVo0QL29vZYs2YNxGIx1+lUWmX/J4VhOBAXF4eqVavKbZK3RCLB2LFjUb16dWzZskXq7aupqWHWrFk5p5Iw8kVEePHiBdasWYOWLVtCT08P06dPR1ZWFjZt2oQPHz7gxYsXWL16dYUYqu3WrRtEIhG8vb3lHlsgEKBnz57w9PREeHg4nJ2dcfPmTbRu3RrNmzfHrl278OPHD7nnJW0GWmr5nLL+/xRrNUBW3FdIMn5duJMZ8XMLKEXtgufg8v4XpzxYvnw5QkJC2OJDDrGCkmHyEBsbK9fhbnd3d3h7e+PAgQPQ1NSUSQwnJycoKSlh8+bNMmmf+VVWVhZu376NmTNnokGDBmjatCk2bdoEQ0NDHD9+HDExMbh9+zZmzJgBAwMDrtOVqgYNGsDQ0FAuw94FqVOnDpYtW4awsDBcuXIFBgYGmD59OnR1dTFu3Dj4+/uX215LNSUh9AtZOa9q8htAEiQ9v5bzGImykPzqJhR1jfPdMiibvpZqmV6Q82+tWrVC9+7dWS8lh1hByTB5iI2Nldt8tdDQUCxcuBDTpk1Dp06dZBanatWqmDZtGnbt2oW4uDiZxanMEhMTcfr0aQwbNgy1atVC586d4eHhgZ49e+L69ev4/v07Tp8+jaFDh6JatWpcpytT3bp147ygzCYQCNCjRw9cuHAB4eHhWLx4MW7fvg1ra2s0bdoU7u7uSEhI4DrNYrMzrgUBP/9+SiVdY6iatEWCz2HEex9A0vNriDq5GKIf0ajWoeDTZQR8HuyMakk7ZZlasWIFXr9+jXPnznGdSqXEVnkzTB769++PtLQ0XL16VaZxxGIx2rVrh+joaLx48ULmZ+VGR0fDwMAACxYswMqVK2Uaq7L4/PlzznnZ3t7eyMrKQtOmTXPOy27WrFm5H8IuCS8vL/Tp0wehoaFo2LDw03HkTSwW4+bNm9izZw+8vLygqKiIQYMGYeLEibC2ti4X/2ahUUnosvVugdeQKBMJd3+e5S1OT4ZiLQNUbTccKg0K37vz1uz2Mtn2SZa6deuGr1+/4uXLl3I9Y51hBSXD5MnW1hZ6eno4duyYTONs2rQJCxcuhK+vL3777TeZxso2c+ZMHD16FJ8+fSrXe/ZxJXs+ZPbWPk+fPoVQKESHDh1yTqqpV68e12lyLjExEVpaWnBzc8PUqVO5TqdA3759w8GDB7F37158/PgRFhYWmDhxIoYPHy7XhXklMWzvQzwI+w6S4oAjnwf8ZlgDR8flv+l5WfXgwQP89ttvOHPmDH7//Xeu06lUWPnOMHmQx5B3UFAQli5dirlz58qtmASA+fPnIzk5Gbt375ZbzPIuKysLt27dwvTp02FgYIBmzZph8+bNMDIywokTJxATE4ObN29i+vTprJj8H01NTbRp06bMDHsXpHbt2li8eDHev3+P69evw8jICLNnz4auri5Gjx6NBw8elMm5lkFBQXh1cAkkWVlAru3lS4gI4qwMWKQHlsnXXJg2bdqgc+fOWL16NSSSsr/lUUXCCkqGyUNcXJxMF+VkZWVh5MiRMDQ0xJo1a2QWJy9169bFqFGj8OeffyItLU2uscuTHz9+4NSpUxgyZAhq1qyJLl26wNPTEw4ODrhx4wZiYmJw8uRJDBkypMz3YnGlW7duOcdElgd8Ph9du3bFuXPn8PnzZyxfvjxn9MDCwgLbtm1DfHzubXjkTSKRYOvWrWjRogVECZGYal0LKHTNdxHxeDBPf42FTuMxYMCAcjnfesWKFQgMDMSFCxe4TqVSYQUlw/wHEcl8lfcff/yBFy9e4PDhw1BWVpZZnPwsXLgQMTExOHDggNxjl2Xh4eHYsWMHunTpgho1amDIkCEICQnB7Nmz8fTpU3z69Anbt29Hly5doKhY+KbQlV3Xrl2RnJwMPz8/rlMpNh0dHTg7OyM0NBQ3btxA48aNMXfuXOjq6mLkyJG4f/8+Jz14nz9/RpcuXTB79mxMmTIFT548wcIBbTGvq5FU2p/f1RiXty/BuXPncOfOHTRt2hS+vr5SaVte2rZti44dO7JeSjljcygZ5j+Sk5OhoaGB48ePY+jQoVJv/+nTp2jdujUWLVqE1atXS739oho2bBju3buHd+/eQUFBgbM8uEREeP78ec552c+fP4dQKISdnV3Oedn6+vpcp1luSSQSaGtrY+LEiVi3bh3X6ZRaVFQUDh06hD179iAsLAympqaYOHEiRowYIfMpMkSEkydPYurUqdDQ0MChQ4dy7QpxKiAcK7yCIJIQxJKif7QL+DwI+TysdjDDIKv//3kPDw/H8OHDcf/+fSxbtgxLly6V2968pZV9fK6Hhwf69evHdTqVAisomUqLiBASEoLHjx8jPj4ely5dQvPmzaGhoYElS5bg/Pnz6N+/v1RjZmRkoGXLlhAKhfD39+e0lyswMBAWFhY4ePAgRo8ezVke8paZmQkfH5+cRTWfP39GlSpV0LNnT/Tp0wfdu3dHlSpl++zi8mTo0KF4+/YtHj9+zHUqUiORSODt7Y09e/bgwoUL4PP5+P333zFx4kS0bdtW6ivE4+LiMHXq1Jwtp3bs2JHvtlOf41Kx+MIr+L77DpKIwePnf7a1gM+DWEJo17AG1vezgF4e+1qKRCKsX78eq1atgo2NDY4fP15u5gnb2dkhISEBT58+LRer9ss7VlAylUpKSgr279+Pa9euwc/PL2c+lJKSEjIyMgD8nEclkUjA4/FgamqKNm3aYOzYsbC2ti51/EWLFuHPP//E48eP0aRJk1K3V1p9+/bF69evERwcDIEg/w+e8i4hIQFXr16Fp6cnrl69isTEROjr66NPnz7o06cP2rVrx4awZeTQoUMYO3YsoqKiULNmwRtpl0fR0dE4dOgQ9u7di3fv3sHExAQTJ07EyJEjpTJt5saNGxgzZgxSU1Oxa9cuDB48uEj32XTrh/S6VtBsbIPw2NRfluzw8HPTcjujWhhurV+krYHu3buHYcOGITExEXv37sWAAYWfJc61f/75B3Z2drh48SL69OnDdToVHzFMJZCQkEBr164lLS0tEgqF1K1bN1q1ahXduHGDEhISfrn2+vXrBIBcXFxo4sSJZGhoSACoU6dO5O3tTRKJpEQ5PHz4kPh8Pq1fv14aL0kqHj16RADo9OnTXKcidR8/fqRt27ZRp06dSCgUEgBq3rw5rVq1ip4/f17if0emeL5+/UoA6MSJE1ynIlNisZhu375NgwYNIgUFBVJSUqJhw4aRj49PiX7WUlJSyMnJiQBQly5d6MuXL0W+9/379wSAjh8/TkREyelZFPg1gZ5+iqPArwmUnJ5V7HyIiOLi4mjAgAEEgCZMmEDJycklakee2rdvT82aNWO/73LACkqmwrt+/TpVr16dlJSUaNq0afTx48cCrz99+jQByCk0xWIxnT17lpo2bUoAyNHRkRITE4uVQ0pKChkZGVGrVq0oK6tkb+ay0qVLF7K0tCz3b7gSiYQeP35My5cvJ0tLSwJACgoK1LVrV3J3d6fw8HCuU6y0LCwsaNSoUVynITfR0dG0adMmatSoEQEgY2Nj+vPPPykmJqZI9wcEBJCJiQkpKyvTtm3bSCwWFyv+qlWrSF1dnVJSUkqSfoEkEgnt3buXVFRUyMTEhJ4/fy71GNJ0+/ZtAkBeXl5cp1LhsYKSqbAkEglt2rSJ+Hw+de/enSIiIop0386dO0kgEOQqsCQSCZ0+fZo0NDTIzMyMQkNDi5zLzJkzSVlZmV6/fl2s1yAP3t7eBID+/vtvrlMptoyMDLp27RpNnTqV6tatSwCoatWqNHToUDp9+nSu3meGG/PmzaPatWuX+y8txSWRSOjOnTs0ePBgUlRUJEVFRRoyZEi+Ix1ZWVm0Zs0aEgqF1Lx5cwoODi5RTENDQxozZow0XkK+goODydLSkhQVFcnNza3M/ttKJBJq27YttWjRoszmWFGwgpKpkCQSCU2ePJkAkLOzM4lEoiLfu2bNGqpZs2a+zwcHB1OjRo2oatWq9OzZs0Lbyy7YNm/eXOQc5EkikVCbNm3I2tq6XLzhxsXF0bFjx2jgwIGkoaFBAKhevXo0Y8YMun37NmVmZnKdIvMfN27cIAD08uVLrlPhTHR0NLm6upKRkREBICMjI3J1dc3ptXz79i1ZW1sTn8+npUuXUkZGRoni3L9/nwCQt7e3FLPPW1paGs2cOZMAkL29PUVHR8s8ZkncvHmTANClS5e4TqVCYwUlUyG5ubkRANq7d2+x7509ezaZmJgUeE18fDw1b96c6tWrV+CbaGJiIhkYGFD79u2LPWwlT5cvXyYAdOfOHa5TydOHDx9o69at1LFjx5z5kC1btqTVq1fTixcvykUhXJmlpaWRiooKbdq0ietUOCeRSOiff/6hoUOH5vRatmzZkpSUlKhBgwb04MGDUrU/adIk0tfXl+v7zaVLl6hGjRqko6NDN2/elFvcosr+0mxlZcXeK2SIFZRMhXPnzh0SCAQ0Z86cEt0/cuRI+u233wq97tOnT1SzZk2ys7PLt1ds4sSJpKamRu/fvy9RLvIikUioWbNm1KlTJ65TIaKf+QQEBNDSpUvJwsKCAJCioiJ1796ddu7cSZ8/f+Y6RaaYunfvTl26dOE6jTIlMDCQGjduTPh5biIZGhrSxo0bKSoqqkTtpaWlUdWqVWnJkiVSzrRwERER1LlzZ+LxeLRw4cIyN1KQvdjyypUrXKdSYbGCkqlQkpOTqXbt2tSpU6cSL37p1asX9e7du0jX+vj4kFAopA0bNuR67tq1awSAdu3aVaI85O3s2bMEgPz8/DiJn56eTlevXqXJkyeTrq4uAaBq1arR8OHD6cyZM/Tjxw9O8mKkY/PmzaSkpCSThSLl0fnz50lLS4u0tbXJy8uLfHx8aNiwYaSkpEQKCgo0aNAgun37drF6GrN/h9+8eSPDzPMnFovJxcWFhEIhWVlZ0bt37zjJIy8SiYSsra2pdevWrJdSRlhByVQo69evJwUFBQoLCytxGzY2NjR69OgiXz916lSqWrUqxcXF5TwWFxdHderUoS5dupSbNy+RSETGxsbk4OAgt5ixsbF09OhRGjBgAKmrqxMAql+/Ps2aNYvu3LlT5no5mJILCgoiAHT16lWuU+HUjx8/aPTo0QSA+vbtm2vKzPfv32nLli05PZcNGzYkFxeXIvVa9u7dm1q3bi2r1IvM39+fDA0NSV1dnY4ePcp1OjmuXr1KAOjatWtcp1IhsYKSqTDi4uKoatWq5OTkVKp2jIyMijVcHhERQaqqqrRo0aKcx0aMGEFVqlQpd1vVHDp0SOaLJ8LCwmjLli3UoUMHEggEBICsrKxo7dq19PLly3JTgDPFI5FIqG7dujR79myuU+GMj48P1atXjzQ0NOjgwYMF/qxLJBLy9fWlESNG5PRa/v7773Tz5s08ey2joqJIKBSSu7u7LF9CkSUmJtKIESMIAA0fPrxMjDBIJBJq1aoV2djYsPcZGWAFJVNhrFu3jlRUVOjbt2+laqdGjRq0bt26Yt2zaNEiUlVVpaSkJLpw4QIBoEOHDpUqDy5kZmaSgYEBDR48WGptisVievToES1ZsoTMzc1z5kP26NGDdu/eXawNm5nybezYsWRqasp1GnKXnp5OCxYsIB6PR23bti32CEpsbCy5ubmRqakpAaAGDRrQH3/8QZGRkTnXuLm5kYKCAn3//l3a6ZfK0aNHSV1dnQwNDenRo0dcp5OzAPHGjRtcp1LhsIKSqTCaNm1a6kJILBYTn8+n3bt3F+u+sLCwnFXltWrVIgcHh3L7DXjnzp3E5/Pp7du3JW4jLS2Nrly5QpMmTcqZD1m9enUaMWIEnTt3rtgbwzMVQ/ahAeWt5740Xr58SU2aNCEFBQVycXEp1hZm/yWRSOjevXs0cuRIUlZWJqFQSAMGDKAbN25Qs2bNqF+/flLMXHrevXtHVlZWOfPNudzxQiKRUMuWLem3334rt+/RZRUrKJkKIfuosbNnz5aqnbi4OAJAZ86cKfa9zZo1o7p161L16tVL3UvKpbS0NNLR0aGxY8cW677v37/TkSNHyNHRMWc+ZIMGDWj27Nn0zz//lLkTghj5i42NJR6PR/v37+c6FZkTi8Xk6upKioqKZG5uLvUTZeLi4mjbtm1kZmaWs0p8+PDhZfa9JyMjgxYuXEgAqHPnzkU+aEIW/v77bwJAt27dKtZ9WVlZ9OTJE3J3d6fhw4dT06ZNqUqVKgSA9PT0qHnz5jR69Gj666+/6OXLl6X68lAesYKSqRBcXV1JWVmZkpKSStXOu3fvCADdvn272Pf+/vvvBICOHTtWqhzKgk2bNpFQKKRPnz4VeN27d+9o8+bNZGtrmzMfslWrVrRu3ToKDAxkPQBMLq1ataKBAwdynYZMffz4kTp06EA8Ho/mzp1LaWlpMoslkUhyVoerqKiQUCik/v3707Vr18rk3rc3b94kHR0dqlGjBmenc0kkEmrRogW1a9euSO9RUVFR5OzsnFM8Zq9inzhxIk2fPp309PRo9uzZNH78eGrWrFnOe6GWlhatXr2a4uPjZf+iygBWUDIVwuDBg6ldu3albsff358AFLs3ISIiIufUlopwGkhSUhJVr16dpk+f/svjYrGY/Pz8aPHixTk9I0pKStSzZ0/666+/6OvXrxxlzJQXy5Yto2rVqlXI3huJREKHDx8mTU1N0tPTk8tBASKRiHR1dcnJyYni4+Np+/btOXu3GhgY0Lp16zjtDcxLdHQ02dvbEwCaMWOGTAvu/Hh6ehZ6mEN8fDzNmjWLVFRUSF1dnebNm0e+vr6UmppaYNvJycnk7e1N06ZNI2VlZdLQ0KBFixZRcnKytF9GmcIKSqZCaNasGU2YMKHU7Vy5cqXYc7wkEgn16tWLatasKZVh97Ji1apVpKysTB8/fqRLly7RxIkTqXbt2jnzIUeOHEnnz58vda8wU7ncu3ePAJC/vz/XqUhVTEwMOTo6EgAaMWKE3M6Rzz7W8t8LXiQSCT18+JDGjBlDKioqJBAIqF+/fnT16tUyU8hLJBJyc3MjRUVFsrS0LNG55aWN36xZM7K1tc3z+aCgIGrYsCFpamrSihUrKDY2tkRxvn37RvPnzydVVVWytLSkDx8+lDzpMo4VlEy5JxaLSVVVlVxdXUvd1rFjxwhAsTZfPnDgAAEgT09P0tLSojVr1pQ6D67FxMTQjh07SCAQkIKCQs4pHnPmzCEfHx82H5IpsczMTNLU1KTVq1dznYrUXL16lXR0dKh69eolmn9dGsOHDycTE5N8h27j4+PJ3d2dmjRpknPu/Zo1a8rMaMLz58/JxMSEVFRUaM+ePXKdJpO9I8c///zzy+MXL14kdXV1Mjc3l9opZ69evaIGDRqQlpZWiaZUlQesoGTKvejoaAJA58+fL3Vbbm5upKysXOTrP336RBoaGjRq1CgiIrKysqJx48aVOg8uhIaGkqurK7Vv3574fD4BoNq1a5OioiI9ePCAzYdkpKZfv35FOt60rEtOTqapU6cSAOrWrZvci7TExERSVVWl9evXF3qtRCIhPz8/Gjt2LKmqqpJAIKA+ffrQ5cuXOe+1TE5OpgkTJhAAGjBgwC+HRMiSRCIhS0tLsrOzy3ns+vXrxOfzydHRUeqjL7GxsdSlSxdSVFTk7EQyWWIFJVPuRUZGEgDy8vIqdVvLly8nXV3dIl0rFoupc+fOVLdu3ZxJ123atKExY8aUOg95EIvF9PDhQ3J2ds45lUNJSYl69epFe/bsoW/fvlFkZCQpKytXqN4khnu7d+8mgUAgt2FhWfDz86NGjRqRiooKubu7c/KF69ChQ8Tj8QpdPPdfCQkJtHPnTrK0tCQApK+vT6tXr+Z8T9izZ89S1apVSV9fn3x9feUS8/z58wSA7t69S+/evaNq1apRjx49ZFZkp6enk42NDenq6pa5ua2lxQpKptyTZkHp5OREFhYWRbrW3d2dAND169dzHivrBWVqair9/fffNH78eNLW1iYAVKNGDRo9ejR5eHjkOWncycmJqlevzuZKMlKTvW+rh4cH16kUW2ZmJq1YsYIEAgFZWVlxdm42EVHHjh2pY8eOJb5fIpHQo0ePaPz48aSmpkZ8Pp8cHBzo0qVLnPVafvr0idq2bUt8Pp9Wrlwp8+k1YrGYLCwsqEOHDmRmZkaNGjWS+arsiIgIql27NtnY2FSo6UOsoGTKveyC0tPTs9RtDRkyhDp06FDodaGhoaSqqkqTJ0/+5XEbG5syV1DGxMTQwYMHqW/fvqSqqkoAqFGjRjRv3jy6e/duoR8cnz59IqFQKJU5qgyTrVGjRjRp0iSu0yiWN2/ekJWVFQkEAlqxYgWnZ81/+vSJeDye1E7k+vHjB+3atYuaNm2as6/iypUr6fPnz1JpvziysrJo5cqVxOfzqW3btsXugS2us2fPEgASCAQUGBgo01jZ7t+/TwBo3759coknD6ygZMq9tLQ04vF4UvnF7Nq1K/Xv37/Aa0QiEbVt25bq16+fq9fO0NCQ5s6dW+o8Suvt27e0adOmnG/6PB6PbGxsaMOGDRQcHFzs4bkxY8aQjo4OJ9t7MBWTk5MTGRgYlIu5uRKJhNzd3UlFRYUaNWpUJlaor1+/nlRVVaV+6pREIqGAgACaMGFCTq9l79696e+//5Z7b9rdu3dJT0+PqlatSufOnZNZnNjYWBIIBFS3bl2ZxcjLwIEDqW7duhXmfZUVlEyFUL9+fZo/f36p22nZsmWh2w+5uroSj8cjHx+fXx5PT08nPp9Pe/fuLXUexSUWi+nBgwe0cOFCMjExIQCkrKxMvXv3pn379v1y5m9JhISEEJ/Pp127dkkpY6ay8/LyIgClOuJTHr5+/UrdunUjADR16tQysZegRCIhExMTGj58uEzjJCYm0l9//UUtWrQgAFS3bl1asWKFXI/OjIuLy9mOacKECcXagaOolixZQoqKigSAHjx4IPX28xMSEkICgYA2b94st5iyxApKpkLo3r079e7du9Tt1K9fn5ydnfN9Pjg4mJSUlGj27Nm5ngsMDMyZ3C0Pqamp5OXlRePGjaNatWrlzIccM2YMXbx4UeoffIMGDSIDAwNOh/mYiiMpKYkUFBRo+/btXKeSr7Nnz1L16tVJR0eHrly5wnU6OR49ekQA6MaNG3KL+fjxY5o0aRKpq6sTn88ne3t78vLykkuvpUQioT179pCKigo1btxYqsdYikQiqlmzJs2cOZNMTU2pW7duUmu7KIYPH05GRkbloqe+MKygZCqEOXPmUN26dUv9S6mpqUkbN27M87msrCxq2bIlGRsb53lSwtGjRwkAxcTElCqHgkRFRdGBAweoT58+pKKiQgDIyMiI5s+fT/fu3ZPpRPoXL14QADp8+LDMYjCVS4cOHaTyRVDaEhISaMSIEQSAHB0dZfo7XRJOTk6kq6vLycKZpKQk2rNnD7Vs2ZIAUJ06dWj58uUyn+dI9HOz8SZNmpCSkhJt27ZNKkXYP//8QwDIz8+PTp48mfP/5eXSpUsEQG5zN2WJFZRMhXDr1i0CQI8fPy7R/cnpWfT803dSrG1E63YepuT03N+616xZQ3w+P983m/79+1OrVq1KFL8gb968oY0bN9Jvv/1GPB6PeDwetWnThlxcXOS+wrRXr15kYmJSJs8IZsqfP/74g9TV1SkjI4PrVHJ4e3uTnp4eaWpq0pEjR8pcz1FGRgZpaWnRggULuE6Fnjx5QpMnTyYNDQ3i8XjUs2dPunjxokx7LdPS0mjGjBkEgHr16kXR0dGlam/GjBlUp04dEovFJBKJyMTEhHr06CGlbAuXnp5OGhoaFWJrNlZQMhVCZmYmVa9enRYvXlzke95GJtIKz0Bqv/EOGThfonr/+mPgfInab7xDKzwD6W1kIj179owUFBTybT8lJYVUVFRow4YNpX4tIpGI7t27RwsWLCBjY2MCQCoqKuTg4ED79+8v9XzI0nj48CEBkOkEeabyePLkCQEgb29vrlOhtLQ0mjt3LvF4PLK1taWPHz9ynVKeLl68SADo1atXXKeSIykpifbt20etWrUiAKSrq0tLly6V6d/h33//TTVq1KDatWvTrVu3StxOo0aNaOrUqTn/ffz4cbkfDTpw4ECytraWWzxZ4RERgWEqgLFjx8LX1xdv3ryB4P/au/O4GtP3D+Cfc9pU1iIUZWxFJJMlS8iSPbKnZOnYYyzD2Gasw9jGIEOcaLcv2YUia2pQElKoFKV9X8459+8PX/3GtCidejqn6/16ff/49jznuT/HtFznvp/nuhUUSjwvJjkbq84+w52IRCjweRBLSv4R+HJc4dNrqIWeQ9Dta1BRUSly3vHjxzFp0iSEh4ejTZs25c6enZ2N69ev4/z587hw4QI+ffqERo0aYeTIkRg1ahQGDhwINTW1cl+3MgwYMAApKSn4559/wOPxuI5DZJhEIkGTJk0gEAiwefNmznIEBwfD1tYW4eHh2Lx5MxYvXgw+n89ZntKMGTMGUVFR+Oeff7iOUqynT5/i0KFD8PDwQEZGBoYMGYJZs2Zh+PDhUFJSkupYcXFxsLOzg6+vL3755Rds2LChXGPk5uZCXV0dTk5OEAgEAACxWAxDQ0O0adMGFy5ckGrekvzxxx/4448/kJKSItO/U6vnTwwh32HOnDmIiIiAl5dXieccC4zGwF23cf9NEgCUWkz++7hIsyXyBi7D2ZD4oueIxdiwYQMGDBhQrmIyPj4ezs7OGDVqFBo2bIjRo0fj3r17mD59Ou7du4cPHz7A2dkZlpaW1aaYBIDVq1fjyZMnuHr1KtdRiIzj8/mwsLDAtWvXOBlfLBZj69at6Nq1K/h8PoKCgrB06dJqW0wmJSXh4sWLsLOz4zpKiYyNjbFv3z7ExcVBKBQiOTkZVlZW0NPTw5o1a/Du3TupjaWtrQ0fHx9s2bIFO3bsQO/evREZGVnm10dEREAikUBfX7/wawoKClizZg0uXrxYZUW7vr4+0tLSEB9f9O+LLKEZSiJXrKysEBwcjJcvX0JZWfmrY45+r7HDJ7zCY/xs0RYO5v9fOLq5uWHq1KkICAhAt27dSn3ty5cv4e3tDW9vbzx8+BAA0LNnT4waNQqWlpZf/WKrrhhj6NmzJxQUFHDnzh2Z/kRNuPfl5yc+Ph5aWlpVNu7bt29hZ2eHe/fuYdmyZdiwYUOxqw/Vyd9//42FCxciLi6uSv+tKio4OBiHDh2Cu7s7MjIyYGFhgVmzZmHkyJFSm7V89OgRrK2t8enTJ+zfvx82NjbffM3p06cxbty4It97IpEI7du3R7t27eDt7S2VfKUJCwuDoaEhbt26hb59+1b6eJWlen4MI+Q7bdq0Ce/evcOePXu++vqxwGipFJMAsMMnHMcDowEAaWlp+O2332BlZVVsMSkWi3H37l0sW7YM+vr6aNeuHdavXw8tLS04OzsjPj7+q+OygMfjYdWqVbh37x78/f25jkNknIWFBQDg+vXrVTIeYwxHjhyBkZERYmJicOvWLWzdurXaF5PA5+J76NChMlVMAkCnTp3g6OiIuLg4HD58GGlpaRg7diyaN2+OVatW4e3btxUeo1u3bnjy5AksLS1ha2sLOzs7ZGRklPqalJQUAECjRo2++rqioiLWrFmD8+fP48mTJxXO9i1f/nsmJydX+liViWYoidxZunQp9uzZgxs3bqBv376ISc7GwF23kSeSFDk3NyoE8UdXFXudJlN2QEXHoNhjKop8+PxkhgUzJsPf3x9BQUFo3bo1ACArKwvXr1+Ht7c3Ll68iMTERGhpaX11P6Sqqqr03jAHGGMwNjZGkyZNOFuuJPLD2NgYnTp1gqura6WO8+nTJ8yaNQvnzp3DtGnTsHv3btStW7dSx5SWV69ewcDAACdOnMD48eO5jlNhISEhhbOWaWlphbOWlpaWFZ61dHd3x7x589CkSRN4eXmha9euxZ4nFAoxc+ZMFFcGiUQitGvXDh06dMDZs2crlOdbkpKS0LBhQ5w5cwZWVlaVOlZlohlKIne2bt2KPn36YNy4cYiKisKqs88g+sa9knVMRkJzxNKv/qfYoGmJ54skDJP+/Fwwenl5oU6dOhAKhRg5ciQaNmwIKysrBAQEwN7eHvfv38eHDx8Kj8t6MQn8/yylj48PAgMDuY5DZJyFhQV8fHyK/cMuLRcvXkSHDh1w9+5dnDlzBkeOHJGZYhL4XCTVr18fI0eO5DqKVBgZGWHv3r2Ii4uDi4sLMjMzMW7cODRv3hwrV64s172Q/zVlyhQ8efIE9evXR8+ePbF9+3ZIJEUnFEqjqKiI1atX49y5cwgODv7uLDUJzVASuZSYmPj5U2m9pmBDV5d43pcZyoajV0DdoHe5x/khzAO5Ce8QEBAAHo+HXr16wdLSEqNGjfqup71liVgsRrt27WBoaFjpn+CJfLt58yYGDhyIp0+folOnTlK9dmZmJpYuXYqDBw9i+PDhEAqFaNKkiVTHqGwSiQQ//PADhg4digMHDnAdp9KEhobi0KFDcHNzQ2pqKgYOHIhZs2Zh1KhRRe6JL4v8/Hz8+uuv2LZtGwYNGgRXV1c0bfr/EwWHDx+Gvb098vPzi50VFYlE0NfXh7GxMU6fPl2h91aajx8/omnTpvD29oalpWWljVPZaIaSyKWGDRvCz88PSu3MwSTiMr1Gkpdd5nMBgEnEeJ6vgaZNm+LIkSP4+PEj/P398fPPP8t9MQl8fhpyxYoVOHfuHEJDQ7mOQ2RY7969oaqqCh8fH6le98GDBzA2NoaHhwcOHDiACxcuyFwxCQD+/v6Ijo6u1k93S0OHDh2we/duxMbGwtXVFTk5OZgwYQKaN2+OFStWICIiolzXU1ZWxtatW+Hj44Nnz56hU6dOuHz5cuHxFi1aAADevHlT7Ou/zFKeOXMGISEh3/2+vuXLbOyXPLKKZiiJXDPbdhMxKbklHv8yQ8lTVgXLzwF4fKg0N0QD8xlQafrtolC3gSr8l/eXZmSZkp+fj9atW6NPnz7w8PDgOg6RYcOGDUN+fj5u3LhR4WsVFBRgw4YN2Lx5M7p16wY3NzeZ/pA3Y8YM+Pv74/Xr1zWuq8Lz588LZy1TUlIwYMAAzJo1C6NHjy7XrGVCQgKmT5+Oy5cv46effsLWrVuRmJiIZs2alTozWFBQAH19fZiYmODkyZPSeltfOXz4MAQCAbKysmT6liiaoSRyKzNPhPelFJMAAAUlqOn3hMaAmWg09lfU7zMFBZ+iEO/5C/I/fvsenpiUHGTliaSUWPYoKytj+fLlOHr0aIXueSJk8ODBuHPnDrKysip0nRcvXqBHjx74448/sH79ety5c0emi8ns7GycPHkSdnZ2Na6YBABDQ0P89ddfiI2Nhbu7O/Lz8zFx4kQ0a9YMy5cvx+vXr8t0HS0tLVy8eBF//fUX9u/fD1NTU6Snp6N27dp4+fJlia9TUlLCqlWrcOrUqUpbiXn16hX09PRkupgEqKAkciwqKQvfmn6v1awdGlmtQu1OFlBr0x31eoxHE7sdAHhIuf3tJ04ZgHdJFfsDKOvs7e3RqFEjbN26lesoRIYNHjwY+fn5392KSiKRYO/evfjxxx+RmZmJBw8eYM2aNVBUVJRy0qp17tw5ZGZmwtbWlusonFJVVYWtrS38/f0RFhYGGxsbCIVCtG3bFv3798fx48eRl5dX6jV4PB5++uknBAQEICcnB126dIGOjg5u3bpV6uvs7Oygp6eHjRs3SvEd/T8/Pz+YmJhUyrWrEhWURG7lF9MmqCyUGmhDtU135EaHlOmeyu8dR16oqqpiyZIlcHFxwfv377mOQ2SUvr4+mjdv/l1tqGJjYzFkyBAsXLgQM2fOxOPHj9GlS5dKSFn13NzcYGZmhpYtW3Idpdpo164ddu3ahbi4OHh4eEAsFmPSpElo1qwZli1bhvDw0nsOGxsb459//sHkyZPx6tUrXL16FVFRUSWer6ysjFWrVuHkyZMICwsr/HpWngjP49LwJDoFz+PSvmu1Kjo6GoGBgRgzZky5X1vd0D2URG49j0vD8L13v+u1KX6HkR5wBs0XnwBfpfRtDy8t6A1D7XrfNY68SE9Ph56eHqZOnYq//vqL6zhERs2cORN3797Fixcvyvya48ePY86cOVBTU8ORI0cKG6XLg7i4ODRv3vyrvaZJ8V6+fIlDhw7BxcUFycnJ6NevH2bNmoUxY8aU2rR+//79mDdvHjQ1NeHt7Y1evXoVe15+fj7atGkD475D0XncAvi9SkB0cvZXq2A8ALoaajDX14JNd120aVznm7l3796N5cuX49OnTzLVxqo4NENJ5FYLTXV87x1HotSP4Ckqg6dcq9TzeP8bp6arW7cuFi5ciIMHD+LTp09cxyEyavDgwXj58iWio6O/eW5KSgpsbGwwadIkWFhY4NmzZ3JVTAKAl5cXlJSU5KKReWUzMDDAzp07ERsbCy8vLwDA5MmToaOjg6VLl+LVq1fFvm7u3Ln48ccfUVBQADMzM2zcuBFicdGVqfhMEX6Y8SeCdUbC7eE7RP2nmAQ+3wIVlZwN94AoDPrLH1OcAxCTnF1iZrFYDKFQCAsLC5kvJgEqKIkcU1dRhK5G6bOL4uy0Il/Lj3+D7NePUKtFZ/B4pf+I6GqqQV1Ftu/RkpaFCxeCz+fTDCX5bgMGDACfz/9m+6CbN2/CyMgIly5dgqenJ44dOwYNDY0qSlk1GGNwdXXF6NGjUa9ezV4BKY9atWrB2toafn5+ePnyJaZNmwZXV1cYGBigX79+8PLyQm7u1w9rbtiwAenp6bC2tsa6devQv39/xMTEFB4/FhiNgbtuIyb/80Mz39gnA+L/nXD/TRIG7rqNY4HFf0A6evQoQkNDsXp1yb2SZQkteRO5tu78c7gHRBX+gP/XR69V4CspQ0WnHfhq9VCQGIPM4KsAXxFNp+yAUsPmJV6bxyQY1qYO9tn3q6T0smfZsmU4ePAgoqKiUL9+fa7jEBnUo0cPNGvWrNgWLTk5OVi5ciV2796N/v37w8XFBc2bl/wzKsuePn2Kzp0749KlSxg2bBjXcWRaXl4ezp49i4MHD8LPzw8aGhqYOnUqZs6ciXbt2oExBjMzM2RnZ+PPP//ElClTkJWVBaFQiLgGHbHDp/R7MsviZ4u2cDD//24D+fn5MDAwgJGREc6dO1fh61cHNENJ5JpNd90Si0kAUGtrCnF2OtIfnUOyz35kv7wDtbY90XTarlKLSQBgPD4O/jIFvXr1wrFjx5Cfny/t+DJnyZIlyMvLw759+7iOQmTU4MGDcePGjSLLjo8fP4aJiQkOHDiAXbt24fr163JbTAKfH8bR0tKSu2V8LqioqGDSpEnw9fXFq1evYG9vD3d3d7Rv3x59+vSBp6cn1q1bhydPnuDRo0cIDg6Gubk5pq53kkoxCQA7fMJx/F8zlb/++iuioqKwadMmqVy/OqAZSiL3pjgH4P6bpFILy/JS4PNg+oMGxml+wN69e+Hn54cmTZpgzpw5mDVr1lfbe9U08+bNw8mTJ/Hu3Tuoq9P9paR87t+/j169euHBgwcwNTWFWCzG1q1bsXbtWnTo0AEeHh4wNDTkOmalKigoQLNmzWBjY4M///yT6zhyKS8vD97e3nBycoKvry8aNGiAVq1a4fHjx7h8+TLademN/jv9UCBBsf0/Jfk5SA84g7y4V8j/EA5JbiY0hy1CbaOBJY6posjHjcV9cc/nPKytrbFjxw4sXbq0Mt9mlaIZSiL3Nlt1hCJfug2BFfk8/DHGCFZWVvD19UVoaCisrKywfft26OrqwtraGvfv30dN/Ly2fPlypKSk4NChQ1xHITKoW7duqFevHq5du4bIyEj06dMHv/76K5YtW4aAgAC5LyYBwMfHBwkJCXK/1SKXVFRUMGHCBNy8eRPh4eGYOXMm3r17B4lEguHDh8N61wUwHq/EZvKS7HSk3TuKgqQYKGn9UKYxRRKG+S53MWPGDNjY2GDJkiXSfEucoxlKUiMcC4zGijPPpHa9rWM6YmJX3SJfT01NhaurKxwdHREREYHOnTvDwcEB1tbWMr8LQnlMmzYN169fx5s3b0pt2UFIccaNG4enT5/i48ePaNy4Mdzc3Eps5yKPJk6ciBcvXiA4OLhG7o7Dlfz8fHh5eWHRb3+gvs3OUs9logJIcjOhULsB8j68xkfXxd+cofxCK9AJty8cl7u/CTRDSWqESV11sWTg5xuiK/oZapmFfrHFJADUr18fP/30E169eoUrV65AW1sbAoEAzZo1wy+//IJ3795VaGxZsWLFCnz48AGurt/ebYiQf4uPj8fr168RGRmJMWPG4OnTpzWqmExNTYW3t3eN3WqRS8rKypg2bRrm7ToKsNI3rOApKkGhdoNyj8FjEljM3yh3xSRABSWpQTIenkTy1b1QVuBBoZxL4Ap8HlQU+dg6piPmm7f+5vl8Ph9DhgzBxYsXER4ejmnTpuHgwYNo1aoVRo8ejRs3bsj1criBgQHGjh2LrVu3QiSquXudk/I5f/48OnbsWLjj0qhRo1CnzrebQ8uTkydPoqCgAJMnT+Y6So11700q8I2Wcd+L8fi4E5lcKdfmGhWUpEYICAjA2rVrsXB4F/guNUfPlpoA8M3C8svxni01cWNx3xJnJkvTunVr7Ny5E+/fv8f+/fvx5s0bDBo0CO3bt8e+ffuQkZFR/jckA1atWoU3b97g+PHjXEch1VxGRgYEAgFGjRqFHj164MWLF9DX1/9mP0p55ObmhkGDBkFbW5vrKDVSZp4I0aU0I5eG6KTs79qmsbqjgpLIvYyMDEyePBldunTB2rVr0VxDDe723XF9UR9M6a4HPU21Ijvq8ADoaaphSnc93FjcB+723dH8G03Sv0VdXR2zZs1CcHAwbt++jQ4dOuCnn36Cjo4OFixYUOJODrKqc+fOGDZsGDZv3gyJpGbvd05Kdu/ePXTq1AnHjh2DUCjEuXPnoKWlhcGDB+PatWtyPZP/X5GRkbh79y49jMOhqKSsIjvgSBsD8C4pq5JHqXq0xQeRew4ODkhISICPjw+UlJQKv96mcR2sszTEOhgiK0+Ed0lZyBdJoKzIRwtN9UrbAYfH46FPnz7o06cP3r9/DycnJxw8eBCOjo4YNGgQFixYgGHDhkFBQaFSxq9Kq1evRq9eveDt7Q0rKyuu45BqJD8/H+vWrcPWrVthamqK69evo1WrVoXHLSwssGfPHoSHh0NfX5/DpFXHw8MDderUwejRo7mOUmPli6rmw29VjVOVaIaSyLVjx47Bzc0N+/bt++qP1X+pqyjCULseOus2gKF2vSrbTrFZs2bYuHEjoqOj4e7ujrS0NFhaWqJ169bYvn07kpNl+16bnj17om/fvvj9999r1EwTKd3z58/RvXt3bN++HZs2bYK/v3+Rn89+/fpBWVm5xix7M8bg5uaG8ePHQ02tYqsh5PspK1ZNWVRV41Ql+XtHhPxPVFQU5syZg0mTJmHKlClcxymViooKbG1tERAQgICAAPTp0wdr1qyBjo4OBAIBnj59ynXE77Z69Wr8888/uH79OtdRCMckEgl27doFExMT5Ofn49GjR1i5cmWxs/Hq6uro3bs3rl27xkHSqnf//n28efOGlrs5EhMTA1dXV2xZtaTSP/zyALTQlL9NH6igJHJJJBLB1tYW9evXx/79+2Wq/Ua3bt3g6uqKmJgY/Prrr7h27Ro6d+4MMzMzHD9+HAUFBVxHLJeBAweia9eu+P3337mOQjgUExODQYMGYcmSJZg7dy6CgoLQuXPnUl9jYWEBPz8/5OXlVVFK7ri5uUFPTw9mZmZcR6kR4uPjcezYMcyaNQtt2rSBrq4upk+fjrCQJ6iNnEodW1dTrcpWwaoSFZRELm3ZsgX379+Hh4cH6tevz3Wc76KlpYVVq1bh7du3OHXqFBQVFTFp0iTo6elhw4YN+PjxI9cRy4TH42H16tXw9/fH3bt3uY5DqhhjDF5eXujYsSPCw8Nx48YN7Nq1q0x9+AYPHozs7Gzcv3+/CpJyJzc3F8ePH8eUKVPA59Of5cqQkpKCc+fOYeHChejQoQOaNGkCa2tr3L17F4MHD8bp06fx6dMnPH78GON7ti9Ta7n0fy4g9d4xZIZ8Xn3JiXiE1HvHkHrvGCS5xT90o8DnwbytllTfW3VBO+UQufPgwQOYmZlh1apV2LBhA9dxpCo0NBSOjo5wd3dHQUEBxo0bhwULFsDU1LRaz8JKJBIYGRmhefPmuHLlCtdxSBVJTk7G3LlzceLECUyePBmOjo5o0KDszaAlEgm0tbUxbdo0/PHHH5WYlFsnT57EhAkT8OrVK7Rt25brOHIhIyMDd+/eha+vL3x9ffHkyRMwxtCyZUv0798f/fv3R79+/dC0adMir30dn4FBf/l/c4z3f8+AOD2h2GM6c5yhWL9xscduLO6D1lry11+VCkoiV9LT02FsbIzGjRvjzp07UFSUv2UF4PNuGi4uLnB0dERkZCR+/PFHLFiwABMnTqy2OzB4eXnBxsYGQUFBMDEx4ToOqWQ+Pj6YPn06srOzceDAAUycOPG7rjNlyhSEhobiyZMnUk5YfYwcORKJiYl48OAB11FkVk5ODh48eABfX1/4+fnh0aNHEIlE0NbWxoABA2Bubg5zc3O0aNHim9cKDg7GWMdbKNBoAR5fen9DFPg89GypCXf77lK7ZnVCBSWRK1OmTIG3tzeePn2Kli1bch2n0kkkEly7dg179+7FlStXoKmpCYFAgLlz50JPT4/reF8RiUQwMDCAsbExTp06xXUcUkmys7Pxyy+/FLbBOnLkCHR0dL77eh4eHpgyZUrhvt7yJiEhAdra2ti7dy/mzp3LdRyZUVBQgMDAwMIZyPv37yMvLw8NGzaEubl54SxkmzZtyrx6k5+fj82bN+P333+H/o89kT9wOfKl2N1HRZGPG4v7VrincXVFBSWRG19mwNzd3WFra8t1nCoXERGBv//+G4cPH0ZGRgYsLS3h4OCA/v37V5vlcKFQiFmzZuH58+do164d13GIlAUFBcHW1hZRUVHYtm0b5s+fX+F7AuPj49GkSRO5/bnevXs3li1bho8fP0JDQ4PrONWWWCzG06dPCwvIO3fuICsrC3Xr1kW/fv0KC0hDQ8Pv+p57/Pjx54dywsKwatUqrF69GmeCP2LFmWdSew9bx3T8rt3WZAUVlEQuvH37FsbGxhgxYgQ8PT25jsOpzMxMeHp6wtHREaGhoWjXrh0cHBwwZcoUzvdFzs/PR6tWrWBubg43NzdOsxDpEYlE2LJlCzZs2IBOnTrBw8MDBgYGUrt+586d0aFDB7i7u0vtmtWFiYkJWrRogdOnT3MdpVphjOH58+eFS9i3bt1Camoq1NTUYGZmVjgL2blz5wrd2pSXl4eNGzfijz/+QIcOHeDi4gJjY+PC445+r7HDJ7zC72eZhT7mm7eu8HWqMyooicwTiUTo27cv4uLi8PTpU9SrV4/rSNUCYwy3b9+Go6Mjzp07B3V1dUybNg3z5s3jdOeR3bt3Y+nSpQgPD68RtyXIu9evX2PKlCkIDAzE6tWr8euvv361I5U0rFixAi4uLoiLi5Orp6BDQ0PRsWNHnDt3DqNGjeI6DqcYY4iMjCycgfTz80NCQgKUlZXRo0ePwhnIbt26QVlZWSpjBgYGYvr06QgPD8eaNWuwcuXKYr93jwVGY+355xBJGMSSspdMCnweFPk8bLA0lOuZyS+ooCQyb/369diwYQP8/f3Rq1cvruNUSzExMThw4AAOHjyIxMREWFhYYMGCBRg6dGiVb/GYnZ2NFi1aYOzYsdi/f3+Vjk2khzGGgwcPYsmSJWjatCnc3d3Ro0ePShnLz88P/fv3x5MnT76aPZJ1y5cvx+HDhxEXFye1IkmWxMTEFBaQvr6+eP/+PRQUFNC1a9fCArJHjx5S3zkoNzcX69atw/bt22FsbAwXFxd07Nix9KzJ2Vh19hnuRCRCgc8rtbD8ctysdUNstuoot/dM/hcVlESm3b9/H2ZmZvj111+xbt06ruNUe7m5uTh58iT27t2LwMBAtGzZEvPmzcP06dOr9P6tLVu2YN26dXj79i20tbWrbFwiHR8/foS9vT0uX76M2bNnY8eOHahdu3aljZeXlwcNDQ389ttv+OWXXyptnKokFovRvHlzjB07Fnv37uU6TpWIj4+Hn58f/Pz84Ovri4iICPB4PBgbGxcuYZuZmaFu3bqVluHhw4eYPn063rx5g7Vr12LZsmXlmlF/HZ8Bz4Bo+IUnIDopG/8uoHj43LTcvK0WbE115bI1UGmooCQyKy0tDcbGxtDW1sbt27fltkVQZXn06BH27t2LEydOQEFBATY2NnBwcECnTp0qfey0tDTo6enB3t4eO3furPTxiPScOXMGs2bNgqKiIpydnTF8+PAqGXfEiBHIycnBzZs3q2S8yubj44PBgwfj0aNH6Nq1K9dxKkVKSgpu375dOAP5/PlzAEC7du0KZyD79u0LTU3NSs+Sk5ODX3/9Fbt27UKXLl1w+PBhGBoaVuiaWXkivEvKQr5IAmVFPlpoqsvlDjhlxgiRUZMnT2Z169Zlb9684TqKTPv48SPbtGkT09HRYQCYmZkZO378OMvPz6/UcVevXs3U1NTYp0+fKnUcIh1paWls2rRpDAAbPXo0S0hIqNLx9+zZw5SVlVlmZmaVjltZbGxsmIGBAZNIJFxHkZqMjAx2+fJl9vPPP7Mff/yR8Xg8BoC1bNmSCQQC5uXlxeLi4qo81927d1nbtm2ZiooK27p1KysoKKjyDDUBFZREJrm7uzMAzNPTk+sociM/P5+dPHmS9e3blwFg2trabMOGDezDhw+VMt6nT5+YmpoaW7NmTaVcn0jP7du3mZ6eHqtTpw47cuQIJ0XQy5cvGQB28eLFKh9b2tLT05mqqirbvHkz11EqJDs7m928eZOtWbOG9ezZkykqKhb+7rC1tWWHDx9mb9++5SxfZmYm++mnnxiPx2M9evRgL1684CxLTUAFJZE5kZGRrE6dOszW1pbrKHIrODiYzZo1i6mpqTElJSVmY2PDHjx4IPVCYvHixaxevXosLS1Nqtcl0pGbm8uWLVvGeDweMzMz43Q1QCKRMD09PbZgwQLOMkjLkSNHGI/HY1FRUVxHKZf8/Hx27949tnHjRmZubs5UVFQYANawYUM2fvx4tn//fvbq1atqMet669Yt1qpVK1arVi22c+dOJhKJuI4k96igJDKloKCA9ejRg/3www9UhFSB5ORk9ueff7JWrVoxAMzExIS5uLiwnJwcqVw/NjaWKSsrsy1btkjlekR6QkJCmJGREVNSUmJbt26tFn+QZ82axfT19bmOUWHm5uasf//+XMf4JpFIxIKCgti2bdvYkCFDmLq6OgPA6tatyywtLdlff/3FgoODmVgs5jpqoYyMDDZ//nwGgPXu3ZuFh4dzHanGoIKSyJTffvuNKSgosPv373MdpUYRi8Xs0qVLbMiQIYUzEitXrpTKDMvs2bNZo0aNWFZWlhSSkooSi8Vsx44dTFlZmXXo0IE9ffqU60iFTp8+zQCwd+/ecR3lu717944BYC4uLlxHKUIikbBnz56xPXv2sNGjR7P69eszAExNTY1ZWFiwP/74gz169Kja3oN48+ZN1qJFC6aqqsp2795drQrdmoAKSiIz7ty5w/h8Plu/fj3XUWq08PBw9tNPP7G6desyPp/PrKysmK+v73cvc0VGRjIFBQW2e/duKScl5fXu3TvWt29fxuPx2NKlS6U2Ey0tKSkpTEFBgTk5OXEd5bv9/vvvTE1NjaWnp3MdhUkkEvb69Wvm5OTEJk6cyLS0tBgApqyszPr27cvWr1/P7ty5w/Ly8riOWqr09HQ2Z84cBoD17duXRUREcB2pRqK2QUQmpKamwtjYGM2aNcOtW7eoRVA1kJmZCQ8PDzg6OuL58+do37594RaP5e1JOGXKFNy6dQuRkZE1ssEz1xhjcHd3x4IFC1C/fn24urqiX79+XMcqVq9evdC0aVOcOnWK6yjlxhhDu3bt0K1bN862Hv13M3E/Pz/ExMRUSTPxynL9+nUIBAIkJSVh27ZtmDNnjlztpiRTuK1nCfk2iUTCJk2axOrVqyfTS13ySiKRMF9fXzZmzBjG5/NZ3bp12U8//cRevXpV5ms8f/6cAWBCobASk5LifPr0iY0dO5YBYFOmTGGpqalcRyrV+vXrWb169artsmtpAgICGAB2/fr1Khvz48eP7NixY2zWrFmsdevWDADj8XjM2NiYLVmyhF28eFEm70dPTU1lAoGAAWD9+/en9nHVABWUpNpzdXVlANjRo0e5jkK+ISoqiq1cuZI1bNiQAWBDhgxhFy9eLNO9TGPGjGGtW7eWyUJBVl2+fJk1adKEaWhosJMnT3Idp0wePHjAALB79+5xHaXc5s+fz3R0dCr1Aafk5GR29uxZtmDBAmZoaMgAMACsXbt2bP78+ez06dMsMTGx0savCpcvX2bNmjVjderUYU5OTtXiqXJCBSWp5iIiIljt2rWZnZ0d11FIOeTk5DAXFxdmYmJS2Nh4586dLDk5ucTXBAUFMQDMy8urCpPWTJmZmWzu3LmFRT8Xzaa/l0gkYg0aNGBr167lOkq55OXlMQ0NDfbLL79I9br/biZuYmJSbZqJV4bk5OTC5voWFhYy13ZJ3tE9lKTaKigogJmZGT59+oSnT5+iTp2atS+qPGCMISAgAI6Ojjhx4gSUlJRga2uL+fPnw8jIqMj5Q4YMQWxsLIKDg+k+qEoSEBCAKVOm4P3799i5cyfmzJkDHo/HdaxymTBhAmJiYvDgwQOuo5TZuXPnYGVlhdDQ0Apt+Zebm4sHDx4U3gf56NEjiEQiaGtrF94DaW5ujhYtWkgvfDVw4cIFzJ49G1lZWdi1axemT58uc9+3co/jgpaQEq1Zs4YpKCiwhw8fch2FSMHHjx/Zxo0bmba2NgPA+vTpw06ePPnVFo/+/v4MAPP29uYwqXzKz89na9euZQoKCqxr167s5cuXXEf6bocOHWJ8Pr/UGe/qxsrKipmYmJT7dbLUTLwyJCUlMVtbWwaADRs2jMXExHAdiZSACkpSLfn7+zM+n882btzIdRQiZfn5+ezEiROsT58+DADT0dFhGzduZPHx8YwxxszMzFi3bt3k9g8kF16+fMm6du3KFBQU2Lp16yp9n/bKFh0dzQDIzH2fiYmJTElJqUytsf7dTHzo0KEy0Uy8spw9e5Y1btyY1a9fn7m4uNDvhGqOlrxJtZOamopOnTpBT08Pfn5+UFBQ4DoSqSTBwcHYt28fPDw8IBaLMWHCBJiYmGDx4sW4fv06Bg4cyHVEmcYYw/79+/Hzzz+jWbNm8PDwQLdu3biOJRXt27dHr169cOjQIa6jfNO+ffuwaNEixMbGQktL66tjjDE8f/4cfn5+8PX1xa1bt5Camgo1NTX07t27cBm7c+fONaZdWmJiIhYuXIijR49i5MiROHDgALS1tbmORb6BCkpSrTDGMGnSJFy7dg0hISHQ1dXlOhKpAikpKTh8+DD27duHt2/fQk1NDbq6unjy5Alq1arFdTyZFBcXhxkzZuDatWuYN28etm3bBnV1da5jSc2iRYtw5swZREVFVft76bp3747GjRvj/PnzYIwhMjLyq16QCQkJUFZWRo8ePQoLyG7dutXInqynTp3C/PnzIRKJsGfPHkyePLna//cl/8Pd5CghRR05coQBYMePH+c6CuGASCRiFy5cYMbGxgwAq1evHlu1ahWLjo7mOppMOXHiBNPQ0GBNmjRhly9f5jpOpbh8+TIDwF68eMF1lFK9ePGCAWDz589nU6dOZc2bN2cAmIKCAjM1NWWrVq1iN27cqPFbj8bHx7Px48czAMzKyop9+PCB60iknGiGklQbERERMDY2xvjx43HkyBGu4xAOSSQS6OvrQyQSISkpCdnZ2Rg9ejQcHBzQt29fmrEoQWpqKhYsWAAPDw+MHTsWTk5O0NTU5DpWpcjOzkaDBg2wbds2/PTTT1zH+Up8fDxu3boFX19fnDx5EikpKeDxeOjUqVPhDKSZmRnq1q3LdVTOMcZw4sQJODg4AAAcHR0xYcIE+hmXQVRQkmqhoKAAvXr1QnJyMp48eUItggg8PDwwZcoU3L17FyEhIXB0dERYWBg6dOgABwcH2NjYlHuLR3l269Yt2NnZIS0tDY6OjrC1tZX7P8oDBw6EsrIyLl++zGmOlJQU3L59u3AZ+/nz5wCAdu3aISYmBj169MDRo0fltrj/Xh8/fsS8efNw9uxZjB8/Ho6OjkXuMSUyhNP5UUL+Z9WqVUxRUZEFBARwHYVUEwUFBeyHH35g48ePZ4x93uLx5s2bzMrKivH5fFavXj22aNEiFh4eXuI1JBIJS0hIYJGRkSwwMJBFRESwuLg4uXpaNCcnhy1ZsoTxeDzWt2/fGrU96bZt25iqqirLzc2t0nG/NBNftmxZsc3EPT09WVxcHPP19ZXZXX0qk0QiYR4eHkxDQ4M1atRIZp7WJ6WjgpJw7tatW4zH47Hff/+d6yikmjlw4ADj8XhF7pN79+4dW7FiBdPU1GQA2NChQ9mlS5eYWCxmQUFBbPv27Wzs2LGFPS//+z8NDQ02dOhQtmHDBubr6yuzLViePHnCDA0NmbKyMtuxY4fMvo/vFRwczACwGzduVOo4OTk5zNfXl61Zs4b17NmTKSoqMgBMW1ub2drassOHD7O3b98Wed20adNYq1at5OoDTEXFxsaykSNHMgDM2tqaffr0ietIREpoyZtwKiUlBUZGRmjVqhVu3rxJLYLIV/Ly8tCyZUtYWFgUe19tbm4ujh07hj179hQ+EZ6bmws1NTV07doVpqam6NKlCxo0aIDU1FRoaGggIyMDT58+xYMHD/Dw4UOkpqbC0NAQq1atwoQJE2SiNYtYLMaOHTvw66+/ol27dvDw8EDHjh25jlXlGGPQ1tbGlClTsG3bNqldt6CgAIGBgYVL2Pfv30deXh4aNmwIc3Pzwvsg27RpU+JtBVlZWWjSpAmWLVuG3377TWrZZBVjDG5ubli0aBFUVFRw4MABjB49mutYRJq4rWdJTSaRSNi4ceNY/fr16SleUqI///yTKSoqlriUGxYWxrp3714486igoMBUVVXZ7NmzWUhISKnXFovFzN/fnw0dOpQBYK1bt2a+vr6V8Tak5s2bN6x3796Mx+Ox5cuXV/lyb3VjZ2fHOnXqVKFrVEYzcQ8PDwaARUZGViibPIiJiWHDhg1jAJitrS1LSkriOhKpBFRQEs44OzvL1G4XhBuZmZlMU1OTzZs3r8gxb29vVqdOHda+fXt27do1JpFI2IcPH9j69etZ06ZNGQDWr18/durUKVZQUFDqOEFBQaxfv35MQUGB/fXXX9VumVIikbDDhw+z2rVrsxYtWrDbt29zHala8PT0ZADK1WZGIpGw0NBQtmfPHjZ69GhWv359BoCpqakxCwsL9scff7BHjx5983umNBYWFszMzOy7Xy8PJBIJEwqFrG7dukxbW5tduHCB60ikElFBSTgRHh7O1NXV2YwZM7iOQmTAxo0bmYqKCouLiyv82pYtWwp71qWnpxd5TX5+Pjt+/Djr3bs3A8CaNWvGNm3aVLjFY3EKCgrY0qVLGQA2bdo0JhKJKuX9lFdCQgIbPXo0A8CmT5/O0tLSuI5UbcTHxzMAzNXVtcRzJBIJe/36NXNycmITJ05kWlpaDABTVlZmffv2ZevXr2d37txheXl5Usn0/v17xufz2aFDh6RyPVkUFRXFLCwsCn+WZGnfdfJ9qKAkVS4vL4916dKFtWnThmVkZHAdh8iAlJQUVrduXbZs2TLGGGMuLi4MAPv111/LtAz55MkTJhAImKqqKlNWVmZTpkwptaOAu7s74/P5bPny5VJ7D9/rwoULTEtLizVs2JCdOXOG6zjV0o8//shsbGy++lp0dDRzdXUttpn4ypUr2fXr1yutmfi2bdtYrVq1WGpqaqVcvzqTSCTswIEDrE6dOqxZs2Zy21ifFEUFJalyK1asYIqKiuzRo0dcRyEyZOXKlax27drMx8eHqaiosBkzZpR7WTopKYlt376dtWjRggFg3bp1Y25ubsXeh7hz504GgB09elRab6FcMjIy2KxZsxgANnz4cNo5pBQrV65kmpqazMvLi82aNYu1bt268Il+Y2NjtmTJEnbx4sUqmdmVSCTM0NCQTZo0qdLHqm7evn3LBgwYwAAwgUBQIwvqmowKSlKlfH19GY/HY1u2bOE6CpEx8fHxrFatWqxu3brM1NS0Qg+jiEQidv78+cIluUaNGrHVq1ezmJiYwnMkEgmzsbFhqqqqpfa6rAz3799nrVq1YmpqaszJyana3c9ZHSQnJ7OzZ8+yBQsWFH5AAMDatWvH5s+fz06fPs0SExOrPNfjx48ZgBo1MycWi5mjoyNTV1dnurq6zMfHh+tIhANUUJIqk5SUxHR0dFi/fv2qzb1pRLaYmpoyACw0NFRq13zx4gVbsGABq1OnDlNQUGBjx45lt27dYhKJhGVlZbFmzZqxiRMnSm280uTn57M1a9YwPp/PTE1N2evXr6tkXFlQWjPx6dOns1q1arGVK1dyHZMtWrSINW7cuEIP9MiSiIgI1rdvXwaAzZkzp9j7mUnNQAUlqRISiYSNGTOGNWjQ4KtZIELK6tOnT6x27dqMz+ezrVu3Sv366enpzNHRkRkYGDAArGPHjszJyYk5OjoyAOyff/6R+pj/FhYWxkxMTJiioiLbuHFjjSlISlLeZuIjR45k/fr14y4w+/yBQEtLiy1ZsoTTHFVBLBaz3bt3MzU1NdaiRQt28+ZNriMRjlFBSarEoUOHGAB26tQprqMQGfXLL7+w2rVrMxsbG9a4cWOWnZ1dKeNIJBJ248YNNmrUKMbn81ndunVZgwYNmLm5eaWM9+UPc61atZiBgQELDAyslHGqu/z8fHbv3j22ceNGZm5uzlRUVBgA1rBhQzZ+/Hi2f/9+9urVqxKX/x0dHZmSkhKnD/pduHCBAWBPnz7lLENVCA8PL+ye4ODgQA9XEsYYFZSkCrx8+ZKpqakxgUDAdRQio8RiMdPR0WEODg7s9evXjM/nM0dHx0of9+3bt+yXX35hampqDAAbOHAgu3z5stS2OIyJiWEDBw5kANiCBQsq7anj6uhbzcR37dpVrmbi4eHhDACnvQ7Hjx/PjIyMOBu/solEIrZz505Wq1Yt1qpVK3br1i2uI5FqhApKUqny8vKYiYkJa9u2LcvMzOQ6DpFRDx8+ZAAK/4BZW1szXV1dlp+fXyXjv3nzhvF4PKarq1u4o86uXbtYSkrKd1/z2LFjrH79+kxbW5tdu3ZNemGrqZKaiauqqkqlmbhEImE//PADc3BwkHLysklOTmYqKipsx44dnIxf2V68eMF69OjBeDweW7RoEf0+J0VQQUkq1fLly5mSkhILCgriOgqRYcuWLWONGjUqfJjr2bNnDAA7fPhwlWUwMzNjw4cPZ/fu3WPW1tZMUVGRqaurszlz5rBnz56V+TrJycnM2tqaAWATJkyQ223oytJM3N/fX2rNxBljbM6cOaxNmzZSu155ODk5MT6f/1XzfXlQUFDAtm7dylRUVFibNm3Y3bt3uY5EqikqKEmluXnzJuPxeJXyAAWpWbp168ZsbW2/+tqoUaNY27Ztq6xjwJYtW5i6unrhPXxxcXFs3bp1rEmTJoVbPJ4+fbrUGbbr168zHR0dVq9ePebp6Sl37YC4bCbOGGNnzpxhANibN28qbYyS9OrViw0ZMqTKx61MoaGhrGvXrozP57Off/650u5bJvKBCkpSKRITE5m2tjbr37+/1O43IzWTRCJh9erVK9K79NGjRwwAO378eJXk8Pb2ZgDY+/fvv/p6Xl4eO3r0KOvVqxcDwJo3b842b97MEhISCs/Jzs5mP/30EwPA+vfvz6Kjo6skc2X7+PEjO3bsGOfNxL9ITU1lCgoK7MCBA1U2JmOfW+cAYF5eXlU6bmUpKChgv//+O1NWVmYGBgbswYMHXEciMoAKSiJ1EomEWVlZMQ0NjSJ/fAkprw8fPjAAxW47OGjQINapU6cqmel79eoVA1Bqe5THjx+zGTNmsFq1ajFlZWVmZ2fH3N3dWbt27ZiKigrbtWuXTH/A+nczcUNDw2rTTPzfevfuzaysrKp0zLVr17I6derIxUNVISEhzMTEhPH5fLZixQqWk5PDdSQiI6igJFLn5ORUYgFASHn5+/uX2Mzcz8+vyp7szc/PZ4qKimWa/UpMTGR//PEHa9CgAQPA1NTU2JYtWyq0uw8XSmsmbm9vzzw9PavdPYMbNmxgdevWrbI+nl8eBpoxY0aVjFdZ8vPz2YYNG5iSkhIzNDSkrXFJuVFBSaTqxYsXTFVVlc2aNYvrKERO+Pj4MADs3bt3RY5JJBLWs2dPZmpqWiWzlLVr12a7du365nkRERGsZ8+ejMfjsXHjxrH+/fszAExLS4utWbOm2jb3L28z8eooICCAAaiyh0fu3LnzVQcCWfTkyRNmbGzMFBQU2OrVq2Xugw+pHvggREry8vIwefJk6Orq4s8//+Q6DqkBeDweVq9ejYcPH+LWrVtcxwFjDEKhEJ06dcLHjx9x584dnDx5Ejdv3kRYWBgmTJiAv/76Cy1atMCECRPg7+8PxhhneQsKCnD//n1s2rQJ/fv3R/369dG/f38cOHAAOjo62Lt3L16+fIn379/D3d0d06dPR4sWLTjLWxYmJibQ0NDAtWvXqmQ8Nzc36OnpwczMrErGk6b8/HysXbsWXbt2hVgsRkBAADZt2gQVFRWuoxFZxHVFS+THzz//zJSUlNjjx4+5jkLkSGkzlIx9nqU0NjZmAwYMqPQspc1Qfvz4kY0cOZIBYAKBoMQ9jdPS0tjevXuZvr4+A8CMjIzYwYMHq6Svn0gkYv/88w/bvn17kWbiI0eOLHcz8epqwoQJrFu3bpU+TnZ2NqtXrx5bs2ZNpY8lbUFBQaxjx45MUVGRrV27Vqrtm0jNRAUlkYrr168zAGz79u1cRyFyprR7KL84ceIEA8AePnxYaTlKu4fy3LlzrFGjRqxRo0bM29u7TNeTSCTMx8eHWVpaMh6Px+rXr8+WLl3KIiMjpZa5spuJV1fOzs6Mx+NVeo/P48ePMwDs1atXlTqONOXm5rJVq1YxBQUFZmxszJ48ecJ1JCInqKAkFfbp0yfWtGlTNnDgQJmf2SDVT2lPeX8hEomYvr4+GzlyZKXlKO4p7/T0dGZvb88AMEtLSxYfH/9d137z5g1btmwZa9CgAePxeGz48OHs6tWr5f554qKZeHUUExNTJS2lhg8fzkxNTSt1DGkKCAhg7du3Z0pKSmzjxo1VttMUqRmooCQVIpFI2KhRo5impiaLjY3lOg6RQyX1ofwvFxcXBoAFBwdXSo7/9qG8e/cu++GHH5i6ujoTCoVSeSgoKyuLOTs7M2NjYwaAtWnThv31118sNTW1xNeU1Ey8e/fuVdJMvLpq3759sU9ei8Vi9uLFC3bkyBG2ePFiNnXqVAaA2djYsEWLFjGhUMieP3/+zWL+48ePTEFBgf3999+V9RakJicnhy1fvpzx+XxmYmLCQkJCuI5E5BAVlKRC9u/fzwCwc+fOcR2FyLHidsr5r/z8fKanp8cmTZpUKRm2bNnCateuzXJzc9mKFSsYn89nPXv2ZBEREVIfSyKRsLt377KJEycWbvE4d+5cFhoayuLj46tVM/HqavHixaxZs2ZMIpEwiUTC/Pz82KhRowpbOfF4PNa2bVvWoUMHBoDp6+szfX19xufzGQBWr149Nnz4cObj41Psh4Vdu3YxJSWlar915v3795mBgQFTVlZmW7ZskbvbG0j1QQUl+W5hYWFMVVWVzZkzh+soRM79dy/vkuzbt4/x+XwWHh7+1dczcwtYaGwqexyVzEJjU1lmbvn/qJqZmbE+ffowY2NjpqioyDZv3lwl2z4+f/6cTZw4kampqRUWjwCYgYEBmz9/Pjt16hT79OlTpeeQNVevXmUA2IEDB1jPnj0ZANapUye2fv165uPjU+Ksb3p6Ort58ybbtGkTMzExYQBY165dmbe391eFZefOndmYMWOq6u2UW3Z2Nlu6dCnj8Xise/fu7Pnz51xHInKOxxiHPSuIzMrLy0P37t2Rn5+PoKAgqKmpcR2JyLGAgACYmpri1q1b6Nu3b4nn5ebm4ocffsCwYcOwYvNf8AyIht+rBEQnZ+Pfv+h4AHQ11GCurwWb7rpo07hOqePHxcWhWbNmUFBQQNu2beHh4YHOnTtL5839R2ZmJu7evQtfX1/4+vri8ePHYIyhRYsWaNGiBWJjY/H69Wvo6upi7ty5EAgEaNiwYaVkkWXJyclo1KgRJBIJTE1NsWbNGgwbNgw8Hq/M12CM4fr16/j999/h7++PiRMnwtnZGW/evIGRkRG8vb1haWlZie/i+9y9exczZsxAdHQ0Nm3ahMWLF0NBQYHrWETOUUFJvsvSpUvh6OiIgIAAGBsbcx2HyDmJRAI9PT2MHj0ae/fuLfXc37buxsGnGajVojMU+DyIJSX/ivty3Kx1Q2y26ojmGkU/GEVHR8PCwgKvXr3C7NmzsWvXLqiqqlb4PX2Rm5uLBw8eFBaQjx49gkgkgra2Nvr374/+/fvD3Nz8q/6Pjx8/hqOjI7y8vAAA1tbWcHBwgImJidRyybLY2FiMGTMGQUFBMDQ0RHBwcLkKyeKcPHkS06dPR+vWrdGtWzecPXsWsbGxUFZWllLqisvKysLq1auxZ88emJqa4vDhwzAwMOA6FqkhqKAk5ebj44PBgwdj586dWLJkCddxSA2xYsUK7Nu3D2/evEGjRo2KPedYYDTWnn+O3PwC8Phln5FR4POgyOdhvaUhJnXVBfB5dsrLywvz5s1DdnY2unTpggcPHlT4fRQUFCAwMBC+vr7w8/PDvXv3kJeXh4YNG8Lc3Bzm5ubo378/2rZt+80iKDExEc7Ozvj7778RHR0NU1NTLFiwAOPGjatWhU5ViouLQ7du3cDj8TBu3Dg4OTkhOTkZtWrVqvC1nz17hlGjRiEqKgpTpkyBi4tLxQNLye3btzFjxgx8+PABv//+OxYuXEizkqRKUUFJyuXTp08wMjKCkZERrly5Aj6fNlsiVSMxMREtW7aEQCAodicmR7/X2OETXuFxfrZoi8mdNDF37lycOHEC3bt3R0BAAB4/fvxdy9xisRjBwcGFM5D+/v7IyspC3bp10bdv38JZyA4dOnz3z5NYLMbFixexd+9e3Lx5E40bN8asWbMwZ84caGtrf9c1ZVFeXh769u2L9+/f49GjR0hKSoKRkRF8fHwwaNAgqYxx/PhxTJo0CW3atMHTp085v90nMzOz8MOWmZkZnJ2d0aZNG04zkZqJCkpSZowxjBo1Cg8ePEBISAiaNm3KdSRSw2zYsAGbN2/Gq1evoKenV/j1Y4HRWHHmmdTGEd13RVbIdezevRurV69Gz549cfz48TK9ljGGsLCwwgLy1q1bSE1NhaqqKszMzAoLyM6dO0NRUVFqmb8ICwvDvn374Orqiry8PIwZMwYODg7o3bt3hZd9qzuBQAAPDw/cuXMHXbt2BWMMzZo1w+TJk7F9+3apjGFjY4MHDx4gPj4elpaW8PLy4uzf9ebNmxAIBEhISMAff/yB+fPn04d8whkqKEmZ/f3335g/fz7Onz+PkSNHch2H1EAZGRlo3749dHR0cPv2baioqCAmORsDd91GnkhS5Pz8T1FIu+uF/I8REGelgqekAiXN5qjbfQzU2nQvdgzGGPhMjONTO2DP5rU4c+YMgoODS5z1YYwhMjKycAnb19cXCQkJUFZWRo8ePQrvgezevXuVLkOnpaXBzc0Njo6OCA8PR6dOneDg4IDJkydzPqtWGb7cinP48GFMnz698OvTp0/HP//8g5CQkAqPkZ6ejiZNmuC3335Dq1atMGHCBJw5cwZWVlYVvnZ5cyxfvhxOTk7o168fnJ2d0bJlyyrNQMh/UUFJyuT58+fo0qULZsyYgX379nEdh9RggYGBMDMzg42NDYRCIewOP8L9N0nFPnyTExmI9KALUNExgEJtDbCCPGS/uo+898+hMcQBdYyHFDuGAp8HbYVM3N04EUePHsWkSZO+Oh4TE1NYPPr6+iImJgYKCgro0qVL4Qxkz549q0XhJpFIcOPGDTg6OuLixYuoX78+7O3tMXfuXLkpQhhj6Nq1K5SVlXHv3r2vZgyPHTsGa2trxMbGVnj5/8iRI7C3t0dUVBSaN2+OQYMGIS4uDiEhIVV2v6KPjw8EAgFSUlKwbds2zJ49m2YlSbVABSX5ptzcXHTv3h0ikQhBQUFSfcKVkO/h5uaGqVOnYsGazTgvMirXa5lEjA8ui8BEBdCZdaDUc4dKArF/6zokJCR8VUBGREQAAIyNjQsLSDMzM9StW/e731NVePPmDfbv3w9nZ2ekpqZixIgRcHBwwMCBA2W6KDl16hTGjx9fbFupxMREaGlp4ciRI5g6dWqFxjE3N4eCggJu3LgB4POHm27dusHFxaXC1/6WtLQ0LF26FM7Ozhg4cCAOHTr01ZP/hHCNCkryTYsXL8bff/+NwMBAGBmV7483IZVly5Yt2O77DnVNRgC88hVDCSfXI+/jazRf4FHySUyCHwpikOizH6GhoQCAdu3aFS5h9+3bV2b7P2ZnZ8PLywuOjo4IDg5G27Zt4eDggKlTp1b7org4PXr0QO3atXH9+vVij3ft2hVt2rQpbLP0PaKiotCiRQu4urrCzs6u8OujR49GZGQknj2T3j28/3XlyhXMmjULaWlp2LlzJwQCgdzfD0tkj+x+JCVV4urVq/jrr7+wdetWKiZJtbJy5Uq07DWyTMWkJD8X4uw0FKR8QPqjc8h58w9q6XUq/UU8Pt7mqaJ79+7w9PREXFwcwsLC4OjoiLFjx8psMQkAampqEAgEePLkCe7cuQNjY2MsXrwYOjo6mD9/Pl68eMF1xDJ7//49Hj58WOoM4eDBg3H9+nVIJEXvsy0rDw8PqKmpYcyYMV99ferUqQgNDUV4eMU7DPxXSkoKpk2bhmHDhsHQ0BChoaGYOXMmFZOkWqIZSlKihIQEGBkZwdjYGJcvX5bpJTEifzLzROi47hrK8gss6aojMp9e/fx/eHyote0BjaELoFCrdqmv4wEIXTcY6irSfxq7uomNjYWTkxOcnJyQkJCAAQMGwMHBASNHjqzW/Qz37t2LpUuXIiEhAfXr1y/2HH9/f/Tt2xdBQUHf1fydMQYDAwN0794dbm5uXx3Lzs5Go0aN8Ouvv2LFihXf8xaKdeHCBcyePRvZ2dn4888/MX36dCokSbVGFQIpFmMMM2bMgEQigYuLCxWTpNqJSsoqUzEJAHW7joLWpE3QHL4Yqi1NwJgEEBd883UMwLukrArllBU6OjrYsGEDoqOj4enpiaysLFhZWaFly5bYunUrEhMTuY5YLG9vbwwYMKDEYhL4vCRep04dXLt27bvGePToEcLDw79a6v5CTU0NQ4cOhbe393dd+7+SkpJga2sLS0tL/Pjjj3j+/DlmzJhBxSSp9qhKIMXat28fLl26hCNHjqBJkyZcxyGkiPxi2gSVREmzOVRbGKN2xwHQGr8WLD8XCac2oCwLNOUZRx6oqKhg8uTJePDgAQIDA9G/f3+sXbsWzZo1w4wZM/D48WOuI34lODgYPXv2LPUcJSUl9O/f/7sLSjc3N+jo6MDc3LzY4z169EBISEiFltQB4OzZszA0NMSlS5fg6uqKCxcuQEdHp0LXJKSqUEFJiggNDcXPP/8MBwcHDB8+nOs4hBRLWfH7f32pGfRC/ofXECXHVuo4sq5Lly44cuQIYmJisG7dOty4cQMmJibo1asXjh49ivz8fE7zJSUlITExEfr6+t8818LCAvfv30dGRka5xsjLy8OxY8dga2tb4tK/gYEBsrOzERv77e+n4iQmJsLa2hpjxoxB9+7dERYWBjs7O5qVJDKl5v6mJMXKzc2FtbU12rRpg23btnEdh5AStdBUx/f+uWUFeQAASV7py9mMMdhYWsDOzg7bt2/HlStX8P79+zLNbMqTRo0aYcWKFXjz5g3OnDmDWrVqYfLkydDT08O6desQFxfHSa5Xr14BQJkKysGDB0MkEsHPz69cY1y+fBnJycmYMmVKied8Gf/ly5flujbwueVR+/bt4ePjA09PT5w7d452ISMyiQpK8pVffvkFr1+/hpeXF/WbJNWauooimjco/XtUnJVa5GtMLEJWqC94iipQaqhb6usbKBaga2cjvH79Ghs2bMCwYcPQvHlzaGhowMzMDPPmzcPff/+NO3fuICUlpSJvRyYoKirCysoKN2/eRGhoKKysrLBjxw7o6elh0qRJuHfvXpUW219mBHV1S//vCACtWrVCq1atyr3s7ebmBhMTExgaGpZ4zpfxyzNDmZCQgPHjx2P8+PEwMzNDWFgYJk+eTLOSRGbJ/6OLpMyuXLmCPXv2YPfu3ejYsSPXcQgpUVhYGIRCISJei6HUfgB4/OKXIpOuOoLlZ0OleQco1NGEODMFWWG3IEp6jwb97cFXLrkgVeDzMLpbW6yz/LytnkQiQXR0NJ49e4Znz54hNDQUd+7cwaFDhyASiQB8frClY8eO6NixIzp06ICOHTuiXbt2qFWrlvT/EThmaGiIv//+G1u2bIGLiwv27duH3r17w9jYGA4ODrC2tq6ynYLK+tCghYUFfHx8ynzdxMREXLp0CTt27Cj1vPI8Bc8Yw/Hjx+Hg4AAej4fjx49j/PjxVEgSmUdtgwgAID4+HkZGRjAxMcGlS5folxupdjIzM3Hy5EkcOnQIDx48QMOGDTHabg6uK5uW+JqssNvIDLmO/E/vIMnJAF9ZFcpNWqOOycgS9/L+txuL+6C1Vp1Sz8nPz0d4ePhXheazZ8/w7t07AJ+LnTZt2nxVZHbs2BEtW7as1u14yksikeD69etwdHTEpUuX0KBBg8ItHn/44YdKGfPkyZOYMGECUlNTUa9evW+e7+3tXdiIvCzbTu7btw+LFi1CbGwstLS0SjxPLBZDUVERR44cwbRp00o87+PHj5g7dy7OnTuHCRMmwNHREY0aNfpmDkJkARWUBIwxjBgxAkFBQQgJCUHjxo25jkQIgM/fm0FBQRAKhTh69CgyMzMxaNAgCAQCWFpaQkVFBVOcA0rcy/t7KfB56NlSE+723y46S5KRkYHnz59/VWQ+e/assP2Oqqoq2rdvX6TQbNKkicx/oIuMjCzc4jEtLQ0jR44s3OJRmu/tS0GZnJyMBg0afPP89PR0aGpqYs+ePZg7d+43z+/WrRuaNGmC8+fPl3peQUEBlJWVSywoGWPw9PTEwoULoaSkhL///htjx4795viEyBIqKAn27t2LhQsX4tKlSxg2bBjXcQhBcnIyPD09IRQKERISUtiyZvr06UX2L45JzsbAXbeRJ8X2PiqKfNxY3BfNNaS7ZMsYQ0JCQpHZzOfPnyM7OxsAoKGhUWTZ3NDQsEwzcNVNdnY2PD09sXfvXjx79gz6+vpwcHCAnZ2dVLZ4vHPnDvr06YNnz56hQ4cOZXpNnz59oKmpibNnz5Z63osXL9C+fXucPHkS48aNK/Xct2/fomXLlrh69SoGDx781bG4uDjMnj0bFy9exOTJk7F7926Z3mWJkJJQQVnDPXv2DF27dsXs2bOxe/duruOQGkwikeD27dsQCoU4ffo0xGIxLC0tIRAIYGFhUery8LHAaKw4I729lLeO6YiJXb/9oIe0SCQSvH37tkihGR4eDrFYDODzgx//nc3U19eHiopKleX8Xowx3LlzB46Ojjhz5gxUVVUxbdo0zJ8/HwYGBt993U+fPkFLSwunTp0q84zf77//jq1btyIpKQlKSkolnrdq1Srs378fHz9+/Oa/8ZUrVzBs2DC8ffu28AMPYwyurq5YvHgxatWqhQMHDmDUqFFlfm+EyBoqKGuwnJwcdO3aFXw+H48ePZLLBwdI9ffhwwe4uLjA2dkZkZGRaNu2LQQCAezs7Mp1+4Wj32vs8Kn4fsrLLPQx37x1ha8jDXl5eXj58mWRZfOYmBgAn5+6btu2bZFCs0WLFtV2d6v379/DyckJBw8eREJCAgYOHAgHBweMGDGi3PeUMsagqamJn3/+GatWrSrTa4KCgtC1a1f4+/vDzMys2HMkEgn09PQwYsQI7N+//5vX/Ouvv7By5UpkZWWBz+fj/fv3mDVrFq5cuQI7Ozvs2rULGhoa5XpvhMgaKihrsAULFuDQoUMICgoq83IRIdIgEolw5coVCIVCXLp0CUpKSpgwYQIEAgF69+793ffZHQuMxtrzzyGSsHLdU6nA50GRz8MGS8MqnZn8XmlpaYUF5r8LzS+ti9TV1WFoaPhVkdmhQ4dqdX90Xl4eTp06hb179yIgIAB6enqYN28e7O3toampWebr9OvXD3Xr1v3mfY5fiMViNG7cGHPnzsXGjRuLPcfX1xcDBgzA/fv30aNHj29e09raGq9fv0ZgYCAOHz6MJUuWoHbt2jh48CBtDkFqDCooa6hLly5hxIgR2Lt3LxwcHLiOQ2qIyMhIHD58GEeOHMGHDx/QuXNnzJw5E9bW1qXuxVweMcnZWHX2Ge5EJEKBzyu1sPxy3Kx1Q2y26ij1eyarEmMMHz58KFJkhoWFITc3F8DnBuX/nc00NDRE7dq1Oc0eGBiIffv24ejRo+Dz+Zg8eTIcHBzQuXPnb772zz//xKpVq5CYmFjm92FtbY3IyEg8evQIWXkivEvKQr5IAmVFPlpoqmP+bAHu3buH8PDwb364ycvLQ6NGjTBz5kyEhobCx8cHM2bMwM6dO6X2PU2ILKCCsgb6+PEjjIyM0K1bN1y4cEHmnygl1Vtubi7OnTuHQ4cOwdfXF3Xr1oWNjQ0EAgF+/PHHShv3dXwGPAOi4ReegOikbPz7Fx0PgK6mGszbasHWVPebrYFkmVgsRmRkZJFCMyIionDv6R9++OGrQrNDhw7Q19cv9R7DyvDp0ycIhULs378fMTEx6NmzJxYsWIAxY8ZAWVm52Nd8eSDm+PHjmDBhQpnG2ebkjq2n7qHdgHGITcsr8r0hSv2Ijg352O0wDm0al/69cfHiRYwcORKqqqrQ1NTEoUOHMGTIkDK+Y0LkBxWUNYxEIsHw4cPx5MkThISElNpbjZCKePbsGZydneHu7o7k5GSYmZlBIBBg3LhxVdbw+oviZqHUVWr2vg45OTl48eLFV4VmaGho4W4vSkpKMDAwKLJsrqenV+kfQkUiEc6fPw9HR0f4+fmhadOmmD17NmbNmlXstoQmJibQ1tbGhQsXSr3uv2evmURcYkN8AFDgAWKGUmev3759C1NTUyQkJGDmzJnYsWOHVJ5eJ0QWUUFZw+zevRuLFi3ClStX6FM0kbqMjAwcP34cQqEQAQEBaNSoEaZNmwZ7e/sy7bdMuJecnIzQ0NCvZjNDQ0ORlpYGAKhTpw46dOhQpNCsrFY4oaGh2LdvH9zc3JCfn49x48ZhwYIF6NGjR2Fh6+7uDjs7Ozx8+BDduxffO7Si99eutzTEpP/dXyuRSLB//34sW7YMOTk5WLhwIXXJIDUeFZQ1SHBwMLp164Z58+Zh165dXMchcoIxhoCAAAiFQhw7dgzZ2dkYMmQIBAIBRowYUeJSJZEdjDG8f/++yLL5ixcvkJ+fDwBo0qRJkSLT0NBQarPRqamphVs8RkREoHPnzoVbPCorK8PY2BhaWlq4efNmkddKqwPAzxZtMVSXD3t7e9y+fRt6enrg8/l49epVld8eQEh1QwVlDZGTk4MuXbpAUVERAQEB1CKIVFhSUhLc3d0hFArx/Plz6Orqwt7eHtOmTYOubvV/UppUnEgkwuvXr4vMZkZGRoIxBh6Ph1atWhUpNNu0aQNFxe+75UAikcDHxwd79+7FlStX0KBBAwgEArRu3RqzZs3CiRMnMH78+MLzpd2jNP3GftRPCsPMmTOxatUqeHl5wdraWmrXJ0RWUUFZQ8yfPx+HDx/GP//8g/bt23Mdh8goiUQCPz8/CIVCnDlzBowxjBo1CjNnzsSAAQPkam9q8v2ysrIQFhZWpND8+PEjAEBFRQXt2rX76iGgjh07olmzZuW6PzMiIqJwi8f09HQ0bdoUycnJePToETp27FjiLkp5H8KR9ewmcqOfQZQWD75qXaho66N+nylQ0tApcTzGGBQgwf4R2pg4YiDMzMzg7e1dbXt+ElKVqKCsAS5cuABLS0vs27cP8+bN4zoOkUGxsbGFzcffvn0LAwMDCAQCTJkyhR7sImWWmJhY5CGg0NBQZGRkAADq1atXZDazY8eO39ynOysrC56enti9ezfCwsKgpKSEjRs34mmD3ngUlVbknslPZzcj7/0LqBn0hpJWC4gzU5Dx+CJYfi6a2O2AcqMWJY6lwANY/Cso3zuIgIAAmdwSk5DKQAWlnPvw4QOMjIzQo0cPeHt7U4sgUmYFBQW4fPkyhEIhLl++DBUVFUycOBECgQA9e/ak7yUiFYwxREVFFZnNfPnyJQoKCgAAOjo6RQrNdu3aQVVVtci1jh8/jqlTp4LVaQztmcXvcpP7/gVUmrYGT+H/73ssSI5FnLMD1A16oeHIn7+Z++Co5rAwNarAOydEvlBBKcckEgmGDh2KkJAQhISEoFGjRlxHIjIgIiICzs7OcHFxwcePH9GlSxcIBAJMmjSJZmNIlcnPz0d4eHiRQvPt27cAAD6fj9atWxeZzWzVqhXevn0Li5WHIG7Zq9TWQP/14chPAICm00t/YpvPA+xMW2CdpeH3v0FC5EzNbsQm53bv3g0fHx9cu3aNiklSqpycHJw5cwZCoRC3bt1C/fr1YWtrC3t7exgbG3Mdj9RAysrKhe2JJk2aVPj1jIwMhIWFfVVk7tu3D58+fQIA1KpVC+3bt4ey+TLklqOYZIxBnJ0KpYbffqBMwgC/8ASsAxWUhHxBBaWcevr0KVasWIElS5bAwsKC6zikmgoODoZQKISHhwdSU1PRr18/eHh4YMyYMUWWEwmpDurUqYPu3bsX6TcZHx9fOJv5JPQFbiuWbzvJrOe3IM5IQv3eNmU6PzopG1l5ohrfIJ+QL2jJWw5lZ2fDxMQEtWrVwsOHD6GiosJ1JFKNpKen4+jRoxAKhQgKCkLjxo0Lm4+3adOG63iEVNjzuDQM33u3zOcXJMXgg9tSKDfURWObrWVeJr+0oDcMtek2EEIAmqGUS0uXLkVUVBT++ecfKiYJgM/LeQ8ePIBQKMTx48eRm5uLoUOH4uzZsxg+fDg1ZSZyJf8/bYJKI85MQcLJ9eCrqKPh6JXluueyPOMQIu+ooJQz3t7eOHDgAPbv34927dpxHYdw7NOnT4XNx1+8eIEWLVpg5cqVmDZtGpo1a8Z1PEIqhbJi2fpCSnKzEH9iLSS5WWhsuxWKdTQrZRxCagIqKOVIXFwc7O3tMWrUKMyePZvrOIQjEokEN27cgFAoxLlz58Dj8WBlZYU9e/agf//+1ISZyL0WmurgASjtfi4mykfCqQ0QpcSi8aRNUC7Dwzj/xvvfOISQz6iglBMSiQRTp06FsrIyhEIh9QisgWJiYnDkyBEcPnwYUVFRaN++PbZt2wZbW1s0bNiQ63iEVBl1FUXoaqghKjm72ONMIsanc1uRF/cSWmPXQEWn/Ks5uppq9EAOIf9CPw1yYteuXbhx4wZ8fHyoeKhBCgoKcOHCBQiFQly9ehVqamqYNGkSBAIBunfvTh8sSI1lrq8F94CoIrvkAECKrzNyIgKg2robxDmZyAz1++p47Q7mpV5bgc+DeVvaIYqQf6OCUg48fvwYK1euxM8//4xBgwZxHYdUgVevXsHZ2Rmurq5ISEhA9+7dcfDgQUycOBF16tThOh4hnLPprguXB++KPZYf/wYAkBPxCDkRj4oc/1ZBKZYw2JqWb4mcEHlHbYNkXFZWFkxMTKCmpoaHDx9CWVmZ60ikkmRnZ+PUqVMQCoW4c+cOGjRogClTpkAgEKBjx45cxyOk2pniHID7b5KKnaX8Xgp8Hnq21IS7ffdvn0xIDUIzlDJuyZIliI6OxuPHj6mYlFNPnjyBUCiEp6cn0tLS0L9/f3h5ecHKygq1atXiOh4h1dZmq44YuOu2VAtKRT4Pm63oAxwh/0UFpQw7e/YsDh48CCcnJxgYGHAdh0hRWloavLy8IBQK8fjxYzRt2hTz58/HjBkz0KpVK67jESITmmuoYb2lIVaceSa1a26wNERzDTWpXY8QeUFL3jIqNjYWRkZG6Nu3L06fPk0PX8gBxhju3r0LoVCIkydPIj8/H8OHD4dAIMDQoUOhqEif/wj5Ho5+r7HDJ7zC11lmoY/55q2lkIgQ+UMFpQySSCSwsLDAixcvEBISAk3N8jXjJdVLfHw83NzcIBQKER4ejpYtW0IgEGDq1KnQ1tbmOh4hcuFYYDTWnn8OkYSVawlcgc+DIp+HDZaGmNiVHsQhpCQ05SGDdu7cCV9fX1y/fp2KSRklFotx/fp1CIVCeHt7Q0FBAWPHjsWBAwfQt29faj5OiJRN6qqLXq0aYtXZZ7gTkQgFPq/UwvLL8Z4tNbHZqiMtcxPyDTRDKWP++ecfmJqaYsmSJdi6dSvXcUg5RUVFFTYfj4mJQceOHTFz5kzY2NhAQ0OD63iE1Aiv4zPgGRANv/AERCdlf7WjDg+fm5abt9WCrakuWmtRGy5CyoIKShmSlZWFH3/8EXXq1MH9+/fpqW4ZkZ+fj/Pnz0MoFMLHxwfq6uqwtrbGzJkz0aVLF7r/lRAOZeWJ8C4pC/kiCZQV+WihqU474BDyHeinRoYsWrQI79+/pxZBMuLFixeFzccTExPRo0cPCIVCTJgwAbVr1+Y6HiEEn7dpNNSux3UMQmQeFZQy4vTp0xAKhTh06BD09fW5jkNKkJWVhZMnT0IoFOLevXvQ1NSEnZ0d7O3tYWhoyHU8QgghpFLQkrcMeP/+PYyMjGBubo5Tp07REmk1wxjDP//8A6FQCC8vL2RkZGDQoEEQCAQYNWoUVFRUuI5ICCGEVCoqKKs5sViMQYMGITw8HCEhIfTgRjWSkpICT09PCIVCBAcHQ0dHBzNmzMD06dPxww8/cB2PEEIIqTK05F3N7dixA7du3cLNmzepmKwGGGPw9/fHoUOHcPr0aRQUFGDkyJHYvHkzBg8eDAUFBa4jEkIIIVWOZiirscDAQPTs2RM///wztmzZwnWcGu3jx49wdXWFUChEREQEWrduXdh8vEmTJlzHI4QQQjhFBWU1lZmZiR9//BH16tXDvXv36KluDohEIly7dg1CoRAXLlyAkpISxo0bB4FAgD59+tC9rIQQQsj/0JJ3NfXTTz8hLi4Oly5domKyir19+xaHDx/GkSNHEBsbi06dOmH37t2YPHkyGjRowHU8QgghpNqhgrIaOnXqFA4fPgxnZ2e0adOG6zg1Ql5eHs6dOwehUIgbN26gTp06sLGxgUAgwI8//kizkYQQQkgpaMm7momJiYGRkREGDhyIEydOUCFTyZ4/fw5nZ2e4ubkhKSkJvXv3hkAgwLhx46Curs51PEIIIUQmUEFZjYjFYgwYMACRkZEICQmh5dVKkpmZiRMnTkAoFOLBgwdo1KgRpk6dCnt7exgYGHAdjxBCCJE5tORdjWzbtg3+/v7w8/OjYlLKGGMIDAyEUCjE0aNHkZWVBQsLC5w8eRKWlpZ0nyohhBBSATRDWU08evQIvXr1wvLly/H7779zHUduJCcnw8PDA0KhEM+ePUPz5s0Lm4/r6elxHY8QQgiRC1RQVgMZGRno3LkzNDU1cffuXSgpKXEdSaZJJBLcunULQqEQZ86cgVgsxqhRoyAQCDBo0CBqPk4IIYRIGS15VwMLFy5EfHw8rl69SsVkBcTFxcHFxQXOzs548+YN9PX1sWnTJtjZ2UFLS4vreIQQQojcooKSYydOnICLiwuOHDmC1q1bcx1H5ohEIly+fBlCoRCXL1+GsrIyJkyYAFdXV/Tq1YuekieEEEKqAC15cyg6OhpGRkYYPHgwjh07RsVPOURGRhY2H//w4QNMTEwgEAhgbW2NevXqcR2PEEIIqVGooOSIWCyGubk53r17h+DgYHqquwxyc3Nx9uxZCIVC+Pr6ol69erC1tYW9vT06d+7MdTxCCCGkxqIlb4788ccfuHfvHrUIKoNnz55BKBTC3d0dKSkp6NOnD9zc3DB27FioqalxHY8QQgip8WiGkgMPHz5E7969sXLlSmzcuJHrONVSRkYGjh07BqFQiEePHkFLSwvTpk2Dvb092rZty3U8QgghhPwLFZRVLD09HZ07d0ajRo1w584deqr7XxhjePjwIYRCIY4fP46cnBwMGTIEAoEAI0aMoH8rQgghpJqqkUveEokEYWFhCAkJQXp6Oi5cuIBhw4ahXr166NChAzp06ABFxcr5p1mwYAESEhLg4+NDBdL/JCYmwt3dHUKhEGFhYdDT08Mvv/yCadOmoXnz5lzHI4QQQsg31JgZyk+fPuHQoUO4desWAgICkJ6eXuK56urq6NatG/r06YNZs2ZBW1tbKhmOHTsGa2truLq6ws7OTirXlFUSiQQ3b96EUCjEuXPnwBiDlZUVBAIBBgwYAD6fz3VEQgghhJSR3BeUcXFx2LFjB5ycnMDj8WBubo4ePXrA1NQUJiYmqFu3Lng8HhhjyMrKwpMnT/DgwQM8ePAAvr6+yM3NxYwZM/DLL7+gRYsW350jKioKnTp1wtChQ+Hl5VVjWwS9f/++sPn4u3fv0K5dO8ycORO2trZo1KgR1/EIIYQQ8h3kuqA8cOAAFi1aBFVVVSxcuBALFy6EpqZmmV+fnp6Ov//+Gzt37kRqaio2bdqE5cuXl7sYFIlEMDc3R0xMDJ4+fYr69euX853ItoKCAly6dAlCoRBXrlxBrVq1MGnSJAgEApiamtbY4poQQgiRF3JZUObl5WHhwoU4ePAg5s2bh82bN1eo2XVWVhY2bdqEP/74AxMmTMDhw4ehrq5e5tdv3LgR69atw+3bt9G7d+/vziFrXr9+DWdnZ7i4uCA+Ph5du3aFQCDApEmTULduXa7jEUIIIURK5K6gzM3NxeDBg/Hw4UPs378fM2bMkNq1T506hWnTpqFt27a4detWmYqiBw8ewMzMDKtXr8b69eullqW6ysnJwenTpyEUCnH79m3Ur18fU6ZMgb29PTp16sR1PEIIIYRUArkqKBljmD59Oo4fP44bN26gV69eUh8jODgYffr0Qb9+/XD27NlSHx5JT0+HsbExmjRpAn9//0p7crw6CA4OhlAohIeHB1JTU2Fubg6BQAArKyuoqqpyHY8QQgghlUiuHqXds2cPXF1dIRQKK6WYBIBOnTrBy8sLFy5c+OaM4/z585GYmAhPT0+5LCbT09Ph5OSErl27wtjYGKdOncLcuXPx+vVr+Pr6YvLkyVRMEkIIITWA3MxQhoWFwcjICD/99BN27txZ6eNt3rwZq1evxv3799GjR48ix728vGBjYwN3d3fY2tpWep6qwhjD/fv3IRQKceLECeTm5mLYsGGYOXMmhg4dSr01CSGEkBpIbgrKMWPG4MmTJ3j58iVUVFQqfTyJRILOnTujQYMG8PPz++pJ5bdv38LY2BgjRoyAp6dnpWepCp8+fYKbmxuEQiFevnyJH374Afb29pg2bRp0dHS4jkcIIYQQDslFQfno0SN07969yhuGX7x4ESNHjsTVq1cxePBgAJ9bBPXt2xdxcXF4+vRphZ4u55pYLMaNGzcgFArh7e0NHo+HMWPGQCAQwNzcnJqPE0IIIQSAnBSUo0aNQkREBEJCQqCgoFBl4zLG0Lt3b/D5fNy5cwcAsH79emzYsAH+/v6Vdh9nZYuOjsaRI0dw+PBhREdHw9DQsLD5eHn6eBJCCCGkZpD5J0XS09Nx9epVbNu2rUqLSQDg8XiYM2cO7OzsEBsbi3fv3mHDhg347bffZK6YzM/Px4ULFyAUCnHt2jWoqanB2toaAoEA3bp1o+bjhBBCCCmRzM9QHj16FJMnT0ZUVBR0dXWrfPyUlBRoaWlhy5YtcHR0RLNmzXDr1i2Zear71atXcHZ2hqurKxISEmBqagqBQIAJEyagTp06XMcjhBBCiAyQ+YJy/PjxePfuHQIDAznLMHjwYAQHByMnJwfBwcEV2vO7KmRnZ+PUqVMQCoW4c+cONDQ0YGdnB3t7e3To0IHreIQQQgiRMbIxjVaKoKAgjB8/ntMMDRo0QHx8PDw8PKp1Mfn48WMIhUJ4enoiPT0dAwYMwLFjxzBq1CjUqlWL63iEEEIIkVEyXVDm5OQgKioKBgYGnGV48+YNvL29AQD9+/fnLEdJUlNT4eXlBaFQiCdPnkBbWxsLFizAjBkz0LJlS67jEUIIIUQOyHRB+fr1azDGoK+vz8n4IpGo8Mnn2NhYvHr1Ck2bNuUky78xxnDnzh0IhUKcPHkSBQUFGDFiBDZs2IAhQ4bIzP2dhBBCCJENMt1I8N27dwDA2Uzbxo0b8ejRI3h5eQH43NCcS/Hx8di+fTsMDAzQt29f3L9/H2vXrkVMTAzOnTuHESNGUDFJCCGEEKmT6epCLBYDAJSVlat87Lt372LTpk1Yt24d+vTp81WeqiQWi+Hj4wOhUIjz589DQUEB48aNg5OTE/r06UPNxwkhhBBS6WS6oORKamoqbGxs0LNnT6xatYqTDO/evStsPv7+/XsYGRlh165dsLGxQYMGDTjJRAghhJCaSS4KyqrsfMQYw9y5c5GWlgYPDw8oKChU2fh5eXk4f/48hEIhrl+/jtq1a2Py5MkQCAQwMTGh5uOEEEII4YRMF5T169cHACQmJqJhw4ZVMqa7uzuOHTuGo0ePQk9PDwDw6dMnAKi0mcGwsDA4OzvDzc0NiYmJ6NmzJw4fPozx48dDXV29UsYkhBBCCCkrmS4ov7QLevXqVZW0DoqMjMT8+fNhZ2eHSZMmFX791atXX+WRhqysLJw4cQJCoRD3799Hw4YNC5uPt2/fXmrjEEIIIYRUlEwXlE2aNEGdOnUKC7rKVFBQABsbG2hpacHR0fGrY69evQKfz0fr1q0rNAZjDEFBQRAKhTh69CgyMzMxaNAgnDhxApaWllBRUanQ9QkhhBBCKoNMF5Q8Hg/t27fHkydPKn2sDRs2ICgoCPfu3Suyx/WTJ0/QsmXL7y74UlJS4OnpCaFQiODgYDRr1gyLFy/G9OnTq/XOO4QQQgghgIwXlAAwZMgQ7Nq1C/n5+d/VPigrT4R3SVnIF0mgrMhHC011qKt8/c/i7++PzZs3Y/369ejevftXxyQSCby9vTFmzJhyjcsYw+3btyEUCnHq1CmIxWJYWlpiy5YtsLCwgIKCQrnfCyGEEEIIF3isKh+RrgQhISHo1KkTLl++jKFDh5bpNa/jM+AZEA2/VwmITs7Gv/8BeAB0NdRgrq8Fm+66aKgsQqdOndCiRQv4+fkVKfQCAgJgamoKPz8/9OvX75tjf/jwAa6urnB2dkZERATatm0LgUAAOzs7NG7cuOxvnBBCCCGkmpD5gpIxhrZt26JXr15wcXEp9dyY5GysOvsMdyISocDnQSwp+a1/OV43Ow7vz27H07s3oKurW+S8JUuWwMPDAx8+fChxVlEkEuHq1asQCoW4ePEilJSUMGHCBAgEAvTu3Zva/RBCCCFEpsn8Nio8Hg+zZ8+Gp6cnIiMjSzzvWGA0Bu66jftvkgCg1GLy38fTVLSgYfsn7scXPefjx49wcnKCvb19scXkmzdvsGbNGujp6WHkyJGIjo7G3r17C2cpzczMqJgkhBBCiMyT+RlKAMjJyUHr1q3Rr18/eHp6Fjnu6PcaO3zCKzzOzxZt4WDepvD/L1iwAB4eHnjz5k1hD8rc3FycO3cOQqEQN2/eRN26dWFjYwOBQIAff/yxwhkIIYQQQqobmZ+hBABVVVX89ttvOHr0KAICAr46diwwWirFJADs8AnH8cBoAMCLFy/g5OSE5cuXo0GDBggNDcWiRYugo6MDa2tr5Ofnw9XVFR8+fMDff/9NxSQhhBBC5JZczFACn/tE9u7dG3FxcQgKCkLjxo0Rk5yNgbtuI08k+ebr0+4fR6q/O5Qa6kJb8HeJ56ko8nFG8CPGDu4LAFi4cCHc3d3x8OFDNGrUCNOmTcOMGTOqpNE6IYQQQkh1IDcFJQDExsaiS5cuaN26NW7evAl79ye4/ybpm/dLitITEXdoNgAeFOtplVpQKvB5UEyKxLsjP0NRURE5OTkYMmQIBAIBRowY8V2tiwghhBBCZJnM96H8Nx0dHZw+fRr9+vXD0InTEWkwuUyvS/Fzhoq2PphEAklOeqnniiUM4gYtUV/XAPNsx2D69OnFPv1NCCGEEFJTyMU9lP/Ws2dPXLx4EWEFmoBE/M3zc6NDkf3yHhoMmFXmMXhgmL3DA2vXrqVikhBCCCE1nlzNUH5hYWGBH4KAuIyCUs9jEjGSrx9A7U4WUNZqUebrM/Bw+3ViBVMSQgghhMgHuZuhBIDMPBE+fKOYBIDMJ1cgSv+E+n2mlHuM6KRsZOWJviceIYQQQohckcuCMiopC9960kick47UO56o33MiFNTqlXsMBuBdUtZ35SOEEEIIkSdyWVDml6FNUKq/O/iqtVGny8hKHYcQQgghRN7J5T2Uyoql18kFybHIfHoNDQbMhDgjufDrTFwAJhFDlBoPnooaFFTrVGgcQgghhJCaQC4Lyhaa6uABJS57izOSACZByg0npNxwKnI89oA96nSxhMbAkp/85v1vHEIIIYSQmk4uC0p1FUXoaqghKjm72ONKjfTQaMzqIl9P9XeHJD8HGgNnQbF+01LH0NVUg7qKXP7zEUIIIYSUi9xWROb6WnAPiCp2lxwFtXpQa9ujyNfTA70BoNhjX72ez4N5Wy3pBCWEEEIIkXFyexOgTXfdb265+L3EEgZbU2poTgghhBACyPEMZZvGdWDWumGZ9vL+oonNH988R4HPQ8+WmmitVfoDO4QQQgghNYXczlACwGarjlDk86R6TUU+D5utOkr1moQQQgghskyuC8rmGmpYb2ko1WtusDREcw01qV6TEEIIIUSWyXVBCQCTuuriZ4u2UrnWMgt9TOxK904SQgghhPwbjzFWOU+uVDPHAqOx9vxziCSsXA/rKPB5UOTzsMHSkIpJQgghhJBi1JiCEgBikrOx6uwz3IlIhAKfV2ph+eW4WeuG2GzVkZa5CSGEEEJKUKMKyi9ex2fAMyAafuEJiE7K/mpHHR4+Ny03b6sFW1NdepqbEEIIIeQbamRB+W9ZeSK8S8pCvkgCZUU+Wmiq0w44hBBCCCHlUOMLSkIIIYQQUjFy/5Q3IYQQQgipXFRQEkIIIYSQCqGCkhBCCCGEVAgVlIQQQgghpEKooCSEEEIIIRVCBSUhhBBCCKkQKigJIYQQQkiFUEFJCCGEEEIqhApKQgghhBBSIVRQEkIIIYSQCqGCkhBCCCGEVAgVlIQQQgghpEKooCSEEEIIIRVCBSUhhBBCCKkQKigJIYQQQkiFUEFJCCGEEEIqhApKQgghhBBSIVRQEkIIIYSQCqGCkhBCCCGEVAgVlIQQQgghpEKooCSEEEIIIRVCBSUhhBBCCKkQKigJIYQQQkiFUEFJCCGEEEIqhApKQgghhBBSIVRQEkIIIYSQCqGCkhBCCCGEVAgVlIQQQgghpEKooCSEEEIIIRVCBSUhhBBCCKkQKigJIYQQQkiFUEFJCCGEEEIqhApKQgghhBBSIVRQEkIIIYSQCqGCkhBCCCGEVAgVlIQQQgghpEL+DwDZ6uFfN6X6AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 30
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.ProbDist\n",
    "\n",
    "Establishes a distance-based connection pattern between two populations,\n",
    " where the connection probability is contingent upon whether the actual \n",
    " distance between neurons is less than or equal to a given dist. \n",
    " If the distance between two neurons is less than or equal to dist, \n",
    " they have a connection probability of prob. For pre-synaptic neurons, \n",
    " only a proportion specified by pre_ratio will attempt to connect to\n",
    "  post-synaptic neurons.\n",
    "\n",
    "Specifically, Given two points, $P_1$ and $P_2$ in $n$ -dimensional space:\n",
    "\n",
    "$$ P_1 = (x_{11}, x_{12}, \\dots, x_{1n}) $$\n",
    "$$ P_2 = (x_{21}, x_{22}, \\dots, x_{2n}) $$\n",
    "\n",
    "The distance $d$ between them in an $n$ -dimensional space can be computed as:\n",
    "\n",
    "$$ d = \\sqrt{(x_{11} - x_{21})^2 + (x_{12} - x_{22})^2 + \\dots + (x_{1n} - x_{2n})^2} $$\n",
    "\n",
    "In a vectorized form, this can be expressed as:\n",
    "\n",
    "$$ d = \\sqrt{\\sum_{i=1}^{n}(x_{1i} - x_{2i})^2} $$\n",
    "\n",
    "This general formula calculates the Euclidean distance between two points in \\( n \\)-dimensional space and is valid for any dimension \\( n \\).\n",
    "\n",
    "In the context of the provided code:\n",
    "- $P_1$ would be the position of the pre-synaptic neuron.\n",
    "- $P_2$ would be the position of each post-synaptic neuron being considered.\n",
    "\n",
    "---\n",
    "\n",
    "The decision to connect the neurons is then based on:\n",
    "1. The distance $d$ should be less than or equal to `dist`.\n",
    "2. Random generation based on the `prob` parameter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ProbDist` is inherited from `TwoEndConnector`, and it receives four settings:\n",
    "\n",
    "- `dist`: (float, int) The maximum distance between two points.\n",
    "- `prob`: (float) The connection probability, within 0. and 1.\n",
    "- `pre_ratio`: (float) The ratio of pre-synaptic neurons to connect.\n",
    "- `seed`: (optional, int) The random seed.\n",
    "- `include_self`: Whether include the point at the same position."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:30.307391Z",
     "start_time": "2025-10-06T05:18:29.492598Z"
    }
   },
   "source": [
    "conn = bp.connect.ProbDist(dist=10, prob=0.5, pre_ratio=0.5, seed=1234, include_self=True)\n",
    "conn(pre_size=100, post_size=100)\n",
    "mat = conn.require(\"conn_mat\")"
   ],
   "outputs": [],
   "execution_count": 31
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.SmallWorld"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SmallWorld` is a connector class to help build a [small-world network](https://en.wikipedia.org/wiki/Small-world_network) [1].  small-world network is defined to be a network where the typical distance L between two randomly chosen nodes (the number of steps required) grows proportionally to the logarithm of the number of nodes N in the network, that is:\n",
    "\n",
    "$$\n",
    "L\\propto \\log N\n",
    "$$\n",
    "\n",
    "[1] Duncan J. Watts and Steven H. Strogatz, Collective dynamics of small-world networks, Nature, 393, pp. 440–442, 1998."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently, `SmallWorld` only support a one-dimensional network with the ring structure. It receives four settings:\n",
    "\n",
    "- `num_neighbor`: the number of the nearest neighbors to connect.\n",
    "- `prob`: the probability of rewiring each edge.\n",
    "- `directed`: whether the edge is the directed (\"directed=True\") or undirected (\"directed=False\") connection.\n",
    "- `include_self`: whether allow to connect to itself."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:31.105253Z",
     "start_time": "2025-10-06T05:18:30.322462Z"
    }
   },
   "source": [
    "conn = bp.connect.SmallWorld(num_neighbor=5, prob=0.2, directed=False, include_self=False)\n",
    "conn(pre_size=10, post_size=10)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False,  True,  True, False, False, False, False, False, False,\n",
       "        False],\n",
       "       [ True, False,  True,  True, False, False,  True, False, False,\n",
       "         True],\n",
       "       [ True,  True, False, False,  True, False, False, False,  True,\n",
       "         True],\n",
       "       [False,  True, False, False,  True,  True, False, False,  True,\n",
       "        False],\n",
       "       [False, False,  True,  True, False, False, False, False,  True,\n",
       "         True],\n",
       "       [False, False, False,  True, False, False,  True,  True, False,\n",
       "        False],\n",
       "       [False,  True, False, False, False,  True, False, False, False,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True, False, False,  True,\n",
       "         True],\n",
       "       [False, False,  True,  True,  True, False, False,  True, False,\n",
       "         True],\n",
       "       [False,  True,  True, False,  True, False,  True,  True,  True,\n",
       "        False]], dtype=bool)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:31.344252Z",
     "start_time": "2025-10-06T05:18:31.111450Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(conn.require('conn_mat'))\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 33
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.ScaleFreeBA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ScaleFreeBA` is a connector class to help build a random scale-free network according to the [Barabási–Albert preferential attachment model](https://en.wikipedia.org/wiki/Barab%C3%A1si%E2%80%93Albert_model) [2]. `ScaleFreeBA` receives the following settings:\n",
    "\n",
    "- `m`: Number of edges to attach from a new node to existing nodes.\n",
    "- `directed`: whether the edge is the directed (\"directed=True\") or undirected (\"directed=False\") connection.\n",
    "- `seed`: Indicator of random number generation state.\n",
    "\n",
    "[2] A. L. Barabási and R. Albert “Emergence of scaling in random networks”, Science 286, pp 509-512, 1999."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:31.765418Z",
     "start_time": "2025-10-06T05:18:31.348543Z"
    }
   },
   "source": [
    "conn = bp.connect.ScaleFreeBA(m=5, directed=False, seed=12345)\n",
    "conn(pre_size=10, post_size=10)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False, False, False, False, False,  True,  True,  True, False,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True,  True,  True,  True,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True,  True, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False,  True, False, False, False,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True,  True,  True,  True,\n",
       "        False],\n",
       "       [ True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "         True],\n",
       "       [ True,  True,  True, False,  True,  True, False,  True,  True,\n",
       "         True],\n",
       "       [ True,  True, False, False,  True,  True,  True, False,  True,\n",
       "        False],\n",
       "       [False,  True, False, False,  True,  True,  True,  True, False,\n",
       "        False],\n",
       "       [ True,  True, False,  True, False,  True,  True, False, False,\n",
       "        False]], dtype=bool)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:32.593387Z",
     "start_time": "2025-10-06T05:18:32.314067Z"
    }
   },
   "source": [
    "# Using NetworkX to visualize network connection\n",
    "G = nx.from_numpy_array(conn.require('conn_mat'))\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 35
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.ScaleFreeBADual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`ScaleFreeBADual` is a connector class to help build a random scale-free network according to the dual Barabási–Albert preferential attachment model [3]. ScaleFreeBA receives the following settings:\n",
    "\n",
    "- `p`: The probability of attaching $m_1$ edges (as opposed to $m_2$ edges).\n",
    "- `m1` : Number of edges to attach from a new node to existing nodes with probability $p$.\n",
    "- `m2`: Number of edges to attach from a new node to existing nodes with probability $1-p$.   \n",
    "- `directed`: whether the edge is the directed (\"directed=True\") or undirected (\"directed=False\") connection.\n",
    "- `seed`: Indicator of random number generation state.\n",
    "\n",
    "[3] N. Moshiri. \"The dual-Barabasi-Albert model\", arXiv:1810.10538."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:32.747701Z",
     "start_time": "2025-10-06T05:18:32.599021Z"
    }
   },
   "source": [
    "conn = bp.connect.ScaleFreeBADual(m1=3, m2=5, p=0.5, directed=False, seed=12345)\n",
    "conn(pre_size=10, post_size=10)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False, False, False, False, False,  True, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False,  True,  True, False,  True,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True,  True, False,  True,\n",
       "         True],\n",
       "       [False, False, False, False, False,  True, False, False, False,\n",
       "        False],\n",
       "       [False, False, False, False, False,  True, False,  True, False,\n",
       "        False],\n",
       "       [ True,  True,  True,  True,  True, False,  True,  True,  True,\n",
       "         True],\n",
       "       [False,  True,  True, False, False,  True, False,  True, False,\n",
       "         True],\n",
       "       [False, False, False, False,  True,  True,  True, False, False,\n",
       "        False],\n",
       "       [False,  True,  True, False, False,  True, False, False, False,\n",
       "         True],\n",
       "       [False,  True,  True, False, False,  True,  True, False,  True,\n",
       "        False]], dtype=bool)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### brainpy.connect.PowerLaw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`PowerLaw` is a connector class to help build a random graph with powerlaw degree distribution and approximate average clustering [4]. It receives the following settings:\n",
    "\n",
    "- `m` : the number of random edges to add for each new node\n",
    "- `p` : Probability of adding a triangle after adding a random edge\n",
    "- `directed`: whether the edge is the directed (\"directed=True\") or undirected (\"directed=False\") connection.\n",
    "- `seed` : Indicator of random number generation state.\n",
    "\n",
    "[4] P. Holme and B. J. Kim, “Growing scale-free networks with tunable clustering”, Phys. Rev. E, 65, 026107, 2002."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:32.902906Z",
     "start_time": "2025-10-06T05:18:32.752145Z"
    }
   },
   "source": [
    "conn = bp.connect.PowerLaw(m=3, p=0.5, directed=False, seed=12345)\n",
    "conn(pre_size=10, post_size=10)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False, False, False,  True, False, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False,  True,  True,  True,  True,  True, False,\n",
       "        False],\n",
       "       [False, False, False,  True,  True,  True,  True,  True, False,\n",
       "        False],\n",
       "       [ True,  True,  True, False,  True, False, False, False,  True,\n",
       "        False],\n",
       "       [False,  True,  True,  True, False,  True, False,  True,  True,\n",
       "         True],\n",
       "       [False,  True,  True, False,  True, False,  True, False,  True,\n",
       "         True],\n",
       "       [False,  True,  True, False, False,  True, False, False, False,\n",
       "        False],\n",
       "       [False,  True,  True, False,  True, False, False, False, False,\n",
       "        False],\n",
       "       [False, False, False,  True,  True,  True, False, False, False,\n",
       "         True],\n",
       "       [False, False, False, False,  True,  True, False, False,  True,\n",
       "        False]], dtype=bool)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 37
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encapsulate your existing connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BrainPy also allows users to encapsulate existing connections with convenient class interfaces. Users can provide connection types as:\n",
    "- Index projection;\n",
    "- Dense matrix;\n",
    "- Sparse matrix.\n",
    "\n",
    "Then users should provide `pre_size` and `post_size` information in order to instantiate the connection. In such a way, based on the following connection classes, users can generate any other synaptic structures (such like `pre2post`, `pre2syn`, `conn_mat`, etc.) easily."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `bp.conn.IJConn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, let’s take a simple connection as an example. In this example, we create a connection which receives users’ handful index projection by using `bp.conn.IJConn`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:32.942403Z",
     "start_time": "2025-10-06T05:18:32.907664Z"
    }
   },
   "source": [
    "pre_list = np.array([0, 1, 2])\n",
    "post_list = np.array(([0, 0, 0]))\n",
    "conn = bp.conn.IJConn(i=pre_list, j=post_list)\n",
    "conn = conn(pre_size=5, post_size=3)"
   ],
   "outputs": [],
   "execution_count": 38
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:33.104868Z",
     "start_time": "2025-10-06T05:18:32.945669Z"
    }
   },
   "source": [
    "conn.requires('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [ True, False, False],\n",
       "       [False, False, False],\n",
       "       [False, False, False]], dtype=bool)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 39
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:33.710833Z",
     "start_time": "2025-10-06T05:18:33.110333Z"
    }
   },
   "source": [
    "conn.requires('pre2post')"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adadu\\miniconda3\\envs\\bdp\\Lib\\site-packages\\jax\\_src\\ops\\scatter.py:108: FutureWarning: scatter inputs have incompatible types: cannot safely cast value from dtype=int32 to dtype=uint32 with jax_numpy_dtype_promotion='standard'. In future JAX releases this will result in an error.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Array([0, 0, 0], dtype=int32), Array([0, 1, 2, 3, 3, 3], dtype=int32))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 40
  },
  {
   "cell_type": "code",
   "metadata": {
    "scrolled": true,
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:33.822929Z",
     "start_time": "2025-10-06T05:18:33.717235Z"
    }
   },
   "source": [
    "conn.requires('pre2syn')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([0, 1, 2], dtype=int32), Array([0, 1, 2, 3, 3, 3], dtype=int32))"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 41
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `bp.conn.MatConn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In next example, we create a connection which receives user's handful dense connection matrix by using `bp.conn.MatConn`."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:33.918861Z",
     "start_time": "2025-10-06T05:18:33.913208Z"
    }
   },
   "source": [
    "bp.math.random.seed(123)\n",
    "conn = bp.connect.MatConn(conn_mat=np.random.randint(2, size=(5, 3), dtype=bp.math.bool_))\n",
    "conn = conn(pre_size=5, post_size=3)"
   ],
   "outputs": [],
   "execution_count": 42
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:33.993732Z",
     "start_time": "2025-10-06T05:18:33.987867Z"
    }
   },
   "source": [
    "conn.requires('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[False,  True,  True],\n",
       "       [ True,  True,  True],\n",
       "       [ True,  True,  True],\n",
       "       [False,  True,  True],\n",
       "       [False, False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 43
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:35.481693Z",
     "start_time": "2025-10-06T05:18:34.035546Z"
    }
   },
   "source": [
    "conn.requires('pre2post')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([1, 2, 0, 1, 2, 0, 1, 2, 1, 2, 2], dtype=int32),\n",
       " Array([ 0,  2,  5,  8, 10, 11], dtype=int32))"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 44
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:35.636341Z",
     "start_time": "2025-10-06T05:18:35.519242Z"
    }
   },
   "source": [
    "conn.require('pre2syn')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int32),\n",
       " Array([ 0,  2,  5,  8, 10, 11], dtype=int32))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 45
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `bp.conn.SparseMatConn`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In last example, we create a connection which receives user's handful sparse connection matrix by using `bp.conn.sparseMatConn`"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:35.686664Z",
     "start_time": "2025-10-06T05:18:35.658537Z"
    }
   },
   "source": [
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "conn_mat = np.random.randint(2, size=(5, 3), dtype=bp.math.bool_)\n",
    "sparse_mat = csr_matrix(conn_mat)\n",
    "conn = bp.conn.SparseMatConn(sparse_mat)\n",
    "conn = conn(pre_size=sparse_mat.shape[0], post_size=sparse_mat.shape[1])"
   ],
   "outputs": [],
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:36.090568Z",
     "start_time": "2025-10-06T05:18:35.692312Z"
    }
   },
   "source": [
    "conn.requires('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True, False,  True],\n",
       "       [ True, False,  True],\n",
       "       [ True, False,  True],\n",
       "       [False,  True,  True],\n",
       "       [ True,  True, False]], dtype=bool)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:36.110355Z",
     "start_time": "2025-10-06T05:18:36.105291Z"
    }
   },
   "source": [
    "conn.requires('pre2post')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([0, 2, 0, 2, 0, 2, 1, 2, 0, 1], dtype=int32),\n",
       " Array([ 0,  2,  4,  6,  8, 10], dtype=int32))"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.162556Z",
     "start_time": "2025-10-06T05:18:36.151414Z"
    }
   },
   "source": [
    "conn.requires('post2syn')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Array([0, 2, 4, 8, 6, 9, 1, 3, 5, 7], dtype=int32),\n",
       " Array([ 0,  4,  6, 10], dtype=int32))"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using NetworkX to provide connections and pass into `Connector`\n",
    "\n",
    "NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n",
    "\n",
    "Users can design their own complex netork by using `NetworkX`.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.203072Z",
     "start_time": "2025-10-06T05:18:37.199835Z"
    }
   },
   "source": [
    "import networkx as nx\n",
    "G = nx.Graph()"
   ],
   "outputs": [],
   "execution_count": 50
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By definition, a `Graph` is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc).\n",
    "\n",
    "To learn more about `NetowrkX`, please check the official documentation: [NetworkX tutorial](https://networkx.org/documentation/stable/tutorial.html)\n",
    "\n",
    "Using class [`brainpy.connect.MatConn`](https://brainpy.readthedocs.io/en/latest/apis/simulation/generated/brainpy.simulation.connect.MatConn.html) to construct connections is recommended here.\n",
    "- Dense adjacency matrix: a two-dimensional ndarray.\n",
    "\n",
    "Here gives an example to illustrate how to transform a random graph into your synaptic connections by using dense adjacency matrix."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.277485Z",
     "start_time": "2025-10-06T05:18:37.231596Z"
    }
   },
   "source": [
    "G = nx.fast_gnp_random_graph(5, 0.5) # initialize a random graph G\n",
    "B = nx.adjacency_matrix(G)\n",
    "A = np.array(nx.adjacency_matrix(G).todense()) # get dense adjacency matrix of G\n",
    "\n",
    "print('dense adjacency matrix:')\n",
    "print(A)\n",
    "nx.draw(G, with_labels=True)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dense adjacency matrix:\n",
      "[[0 1 1 0 1]\n",
      " [1 0 0 1 0]\n",
      " [1 0 0 0 1]\n",
      " [0 1 0 0 1]\n",
      " [1 0 1 1 0]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users can use class `MatConn` inherited from `TwoEndConnector` to construct connections. **A dense adjacency matrix** should be passed in when initializing `MatConn` class.\n",
    "Note that when calling the instance of the class, users should pass in two parameters: `pre_size` and `post_size`.\n",
    "In this case, users can use the shape of dense adjacency matrix as the parameters."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.326955Z",
     "start_time": "2025-10-06T05:18:37.292106Z"
    }
   },
   "source": [
    "conn = bp.connect.MatConn(A)(pre_size=A.shape[0], post_size=A.shape[1])\n",
    "res = conn.require('conn_mat')\n",
    "\n",
    "print(res)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False  True  True False  True]\n",
      " [ True False False  True False]\n",
      " [ True False False False  True]\n",
      " [False  True False False  True]\n",
      " [ True False  True  True False]]\n"
     ]
    }
   ],
   "execution_count": 52
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customize your connections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BrainPy allows users to customize their connections. The following requirements should be satisfied:\n",
    "\n",
    "- Your connection class should inherit from `brainpy.connect.TwoEndConnector` or `brainpy.connect.OneEndConnector`.\n",
    "- `__init__` function should be implemented and essential parameters should be initialized.\n",
    "- Users should also overwrite `build_csr()`, `build_coo()` or `build_mat()` function to describe how to build your connection.\n",
    "\n",
    "Let's take an example to illustrate the details of customization."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.398180Z",
     "start_time": "2025-10-06T05:18:37.391513Z"
    }
   },
   "source": [
    "class FixedProb(bp.connect.TwoEndConnector):\n",
    "  \"\"\"Connect the post-synaptic neurons with fixed probability.\n",
    "\n",
    "  Parameters\n",
    "  ----------\n",
    "  prob : float\n",
    "      The conn probability.\n",
    "  include_self : bool\n",
    "      Whether to create (i, i) connection.\n",
    "  seed : optional, int\n",
    "      Seed the random generator.\n",
    "  \"\"\"\n",
    "\n",
    "  def __init__(self, prob, include_self=True, seed=None):\n",
    "    super(FixedProb, self).__init__()\n",
    "    assert 0. <= prob <= 1.\n",
    "    self.prob = prob\n",
    "    self.include_self = include_self\n",
    "    self.seed = seed\n",
    "    self.rng = np.random.RandomState(seed=seed)\n",
    "\n",
    "  def build_csr(self):\n",
    "    ind = []\n",
    "    count = np.zeros(self.pre_num, dtype=np.uint32)\n",
    "\n",
    "    def _random_prob_conn(rng, pre_i, num_post, prob, include_self):\n",
    "      p = rng.random(num_post) <= prob\n",
    "      if (not include_self) and pre_i < num_post:\n",
    "        p[pre_i] = False\n",
    "      conn_j = np.asarray(np.where(p)[0], dtype=np.uint32)\n",
    "      return conn_j\n",
    "\n",
    "    for i in range(self.pre_num):\n",
    "      posts = _random_prob_conn(self.rng, pre_i=i, num_post=self.post_num,\n",
    "                                prob=self.prob, include_self=self.include_self)\n",
    "      ind.append(posts)\n",
    "      count[i] = len(posts)\n",
    "\n",
    "    ind = np.concatenate(ind)\n",
    "    indptr = np.concatenate(([0], count)).cumsum()\n",
    "\n",
    "    return ind, indptr"
   ],
   "outputs": [],
   "execution_count": 53
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then users can initialize the your own connections as below:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T05:18:37.469108Z",
     "start_time": "2025-10-06T05:18:37.463614Z"
    }
   },
   "source": [
    "conn = FixedProb(prob=0.5, include_self=True)(pre_size=5, post_size=5)\n",
    "conn.require('conn_mat')"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([[ True, False, False, False, False],\n",
       "       [False, False, False, False,  True],\n",
       "       [ True,  True, False,  True,  True],\n",
       "       [ True,  True, False,  True,  True],\n",
       "       [ True, False, False, False,  True]], dtype=bool)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 54
  }
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